ASSESSMENT OF PORT MARINE OPERATIONS PERFORMANCE … · 2018-12-15 · Delft University of...

Delft University of Technology Faculty of Civil Engineering and Geosciences

Hydraulic Engineering Department

ASSESSMENT OF PORT MARINE OPERATIONS PERFORMANCE BY MEANS OF SIMULATION

Case study: The Port of Jebel Dhanna/Ruwais - UAE

CAROLINA PICCOLI

October 2014

Title: ASSESSMENT OF PORT MARINE OPERATIONS PERFORMANCE BY MEANS OF SIMULATION

Case Study: The Port of Jebel Dhanna/Ruwais - UAE Document: MSc Thesis - Final Report Place and date: Delft, 27/10/2014 Author: Carolina Piccoli Student number: 4255712 Degree: Master of Science University: Delft University of Technology Faculty: Faculty of Civil Engineering and Geosciences Department: Hydraulic Engineering Graduation committee: Prof. ir. T. Vellinga TU Delft /Port of Rotterdam (chairman) ir. B. Wijdeven TU Delft /Royal HaskoningDHV Dr. ir. W. Daamen TU Delft ir. J. M. Valstar Royal HaskoningDHV

In cooperation with:

Assessment of Port Marine Operations Performance by Means Of Simulation

PREFACE

This MSc Thesis presents the work performed during my graduation project as part of the Master Programme of Hydraulic Engineering from the Faculty of Civil Engineering and Geosciences of Delft University of Technology.

This final report represents the closure of a two-year phase full of challenges, new experiences, chaotic feelings, and gratitude. It was a great pleasure to have the opportunity of learning, which is one of the things that I like the most, in a lovely place such as Delft.

I would like to thank all members of the examination committee for the guidance and significant contributions to this thesis. I would like to express my very great appreciation to the chairman, Professor Vellinga for the valuable contribution to the research process; Royal HaskoningDHV for the opportunity, especially Bas Wijdeven for giving me the chance of undertaking this MSc subject and Jacco Valstar, my daily supervisor, for being always available for questions and discussions; and Winnie Daamen for our frequent meetings, constructive remarks on the thesis and the career advices. I would also like to thank Valérie Vanlishout for the support and inspiration. You all have admirable qualities that I want to take as examples for my professional life.

I am also grateful to Dirk-Jan Moens for the FlexSim provision; and to all Talumis staff and Royal HaskoningDHV colleagues that helped me with the modelling assignment.

Last but not least, my grateful thanks are extended to my friends from Brazil that despite the distance will be there when I am back; the friends that I had the opportunity to meet during these two years; and my family for always being so supportive and for keeping me motivated. Special thanks to João for going through all of this together with me. This campaign would have been arduous without you by my side.

Carolina Piccoli October 2014, Delft

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SUMMARY

The assessment of port marine operations performance by means of simulation is treated in this master thesis project. The navigational services provided to vessel at the access channels, from their arrival to the berthing operations and from unberthing to vessels departure, are investigated and evaluated.

Three methods for assessing port performance are available: rules of thumb; queuing theory; and computer simulation. In more complex systems, such as this case study, only a simulation model can give a comprehensive understanding of the contribution of every possible cause of delay.

From the literature review, it is concluded that despite the fact that the use of logistic simulation models is increasing, and many practical applications are identified, the number of publications and academic studies is still limited. By describing extensively the methods used in this graduation project, it is expected that though a case study is analysed, the given research approach can be adapted and harnessed in other problems with similar aims.

In this case study, FlexSim is used as the simulation tool. Since no previous marine operations performance assessment using FlexSim were found in literature, its application for this purpose is tested and evaluated.

Case Study - The Port of Jebel Dhanna/Ruwais

The production of Abu Dhabi National Oil Company is expected to increase and new terminals are planned at the Port of Jebel Dhanna/Ruwais in order to cope with this progress. The marine traffic is expected to be almost doubled from 2014 to 2030.

The approach channels to the port are relatively long and occasionally limited in width and depth. Some sections are restricted to one-way traffic and priority is given to outgoing vessels. Additionally, tidal windows are imposed to deep draught vessels in depth restricted sections. With the expected increase in marine traffic, it is possible that congestion of the access channel will become a limiting factor.

The evolution of the Port of Jebel Dhanna/Ruwais marine operations performance is evaluated based on FlexSim results. The maximum traffic that can be handled with the current nautical infrastructure, without exceeding the acceptable performance limit, is investigated. Once the port bottleneck(s) is(are) identified, measures to improve the performance are proposed and the effects of each alternative are evaluated.

Verbal Model

The implementation of a simulation model requires a prior description of the model by schematizing the reality within the system boundaries. The schematization of the real system is accomplished by defining and describing the processes to be simulated which are: vessels arrival; verifications for clearance; route assignment; vessels sailing; and quay operations. The level of detail of input parameters and the model output requirements are presented.

The model assumptions are also defined; the main ones are: unlimited tugs, pilots, and places at the anchorages; no acceleration and deceleration rates; and same spacing (in minutes) between all vessels in the one-way sections (irrespective of the vessels size or type of cargo).

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Simulation model

The verbal model is implemented in FlexSim by using existing FlexSim objects and by programming new functions whenever required. The programmed functions incorporated to the model are: spacing regulation; the tidal window verification; the prevention of encounters in one-way sections; and the priority for outgoing vessels.

Before performing the simulations, all model functionalities are verified. The validation of the model is given by comparing the turnaround time model output with AIS Sea-web data for the year 2011. The encountered differences are acceptable for this case study application.

Assessment of the Port of Jebel Dhanna/Ruwais Marine Operations Performance

The performance of the Port of Jebel/Dhanna Ruwais marine operations is obtained for the 2014, 2016, 2022 and 2030 traffic and port infrastructure. In 2014 approx. 2000 vessels are expected in one year and in 2030 this number increases to approx. 3600 vessels. However, not only the number of calls changes for every simulated year, but also the fleet mix and the number of terminals.

The results show that the number of vessels delayed due to the traffic and spacing regulations increases with increasing traffic; and so does the contribution of these causes of delay to the total waiting times. The berth unavailability is the most significant source of delay.

The waiting rate of the marine operations (waiting time over turnaround time) does not change significantly from 2014 to 2030 despite of the increasing traffic. Therefore, the performance of the Port of Jebel Dhanna/Ruwais marine operations is acceptable for the forecasted 2030 traffic.

No real bottleneck at the access channels is identified; however, since the results are very sensitive to the fleet mix, more information and monitoring of changes with respect to this input is advisable.

Hypothetical Traffic Increase

In order to be able to identify the marine operations bottleneck, the traffic to terminals with low berth occupancies is artificially increased. By increasing the traffic step by step it is possible to identify when a shift in the main cause of delay occurs. For the simulated fleet mix, the marine operations causes of delay start having the same weight as the berth unavailability cause, when handling approx. 4500 vessels per year (900 vessels more than in 2030). For this situation the performance of the marine operations reach an unacceptable level. When 4800 vessels per year are expected, the existing infrastructure is unable to cope with the demand if the current Port Regulations are applied.

Measures to Reduce Congestion

The effects of six proposed measures to reduce congestion are analyzed. The measures are formed by the combination of three interventions: routing three vessel classes instead of one through a secondary channel; deepening one of the one-way sections; and widening one of the one-way sections.

It is concluded that all simulated measures improve the marine operations performance for the given traffic (4800 vessels per year). However, each measure has a different cost related to it. Therefore, even though the combination of the three interventions is the measure that presents the greatest reduction in waiting times due to marine operations if compared to the no-action measure, only a cost benefit analysis can indicate the best alternative.

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Added Value of Performing Simulations and FlexSim Evaluation

Performing hand calculations can be very useful in giving insight of a system to be studied. However, the conclusions that can be drawn from such a simple estimate are very limited. Once congestion is identified, it is difficult to recognize the bottleneck and to make decisions assisted by hand calculations only. One of the simulation model advantages is the possibility of performing what-if analysis, making decisions on the best investment much more consistent. Moreover, the visualization provided by computer simulations is an advantage, since it improves the communication between the consultant and the client.

After performing all simulations involved in the graduation work, the FlexSim simulation software is considered to be adequate to perform port marine operations simulations. Realistic results are obtained with more than acceptable simulation times and with a moderate time for implementation, which decreases with the modeller increasing experience. However, the possibility of using default FlexSim functions for implementing the traffic regulations would be appreciated.

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TABLE OF CONTENTS

PREFACE ................................................................................................................................................... i

SUMMARY .............................................................................................................................................. iii

TABLE OF CONTENTS .............................................................................................................................. vi

LIST OF FIGURES ................................................................................................................................... viii

LIST OF TABLES ...................................................................................................................................... xii

LIST OF ABBREVIATIONS ....................................................................................................................... xiv

LIST OF SYMBOLS .................................................................................................................................. xiv

1 INTRODUCTION ...............................................................................................................................1

Background information......................................................................................................1 1.1 Port marine operations performance calculation ...............................................................2 1.2 Case study – The Port of Jebel Dhanna/Ruwais ..................................................................2 1.3 Thesis objectives ..................................................................................................................4 1.4 Research approach ..............................................................................................................5 1.5 Report structure ..................................................................................................................6 1.6

2 LITERATURE REVIEW .......................................................................................................................9

Port planning stages ............................................................................................................9 2.1 Simulation models for ports ............................................................................................. 11 2.2 Conclusions ....................................................................................................................... 14 2.3

3 VERBAL MODEL ............................................................................................................................ 15

Problem definition ............................................................................................................ 15 3.1 System definition .............................................................................................................. 17 3.2 System boundaries ........................................................................................................... 18 3.3 Description of navigational services on a high aggregation level .................................... 19 3.4 Detailed description of the Jebel Dhanna/Ruwais navigational services ......................... 21 3.5 Processes definition ......................................................................................................... 24 3.6 Model functionalities ....................................................................................................... 31 3.7 Level of detail of input parameters .................................................................................. 31 3.8 Model output requirements ............................................................................................. 34 3.9

Model assumptions .......................................................................................................... 38 3.10

4 SIMULATION MODEL IMPLEMENTATION ..................................................................................... 41

Introduction to FlexSim .................................................................................................... 41 4.1 Model implementation .................................................................................................... 43 4.2 Model presentation .......................................................................................................... 50 4.3

5 SIMULATION MODEL EXPERIMENTAL SETUP ............................................................................... 51

Traffic forecast .................................................................................................................. 51 5.1 Definition of control variables .......................................................................................... 52 5.2 Sensitivity analysis ............................................................................................................ 53 5.3 Scenario description ......................................................................................................... 62 5.4 Simulation duration, warmup and number of runs ......................................................... 62 5.5

6 SIMULATION MODEL VERIFICATION AND VALIDATION ............................................................... 65

One-way sections regulation and route assignment ........................................................ 65 6.1 Simulation model validation ............................................................................................. 69 6.2

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Conclusions ....................................................................................................................... 73 6.3

7 ASSESSMENT OF THE MARINE OPERATIONS PERFORMANCE ..................................................... 75

Simulation model results .................................................................................................. 75 7.1 Conclusions ....................................................................................................................... 91 7.2

8 COMPARISON TO HAND CALCULATIONS ..................................................................................... 93

Hand calculation results ................................................................................................... 93 8.1 Conclusions ....................................................................................................................... 96 8.2

9 HYPOTHETICAL TRAFFIC INCREASE .............................................................................................. 99

Scenario definition ........................................................................................................... 99 9.1 Percentage of delayed vessels and average waiting times ............................................ 102 9.2 Number of vessels at the anchorages ............................................................................ 105 9.3 KPIs evaluation ............................................................................................................... 106 9.4 Conclusions ..................................................................................................................... 108 9.5

10 MEASURES TO REDUCE CONGESTION ........................................................................................ 111

Measures description ..................................................................................................... 112 10.1 Percentage of delayed vessels and average waiting times ............................................ 114 10.2 Number of vessels at the anchorages ............................................................................ 117 10.3 KPIs evaluation ............................................................................................................... 118 10.4 Conclusions ..................................................................................................................... 121 10.5

11 EVALUATION OF THE USE OF FLEXSIM ....................................................................................... 123

12 CONCLUSIONS AND RECOMMENDATIONS ................................................................................ 125

Conclusions ..................................................................................................................... 125 12.1 Recommendations.......................................................................................................... 128 12.2

13 REFERENCES ............................................................................................................................... 129

APPENDICES ........................................................................................................................................ 131

A EXAMPLES OF MARINE OPERATIONS AT THE PORT OF JEBEL DHANNA/RUWAIS ..................... 133

B FLEXSIM CONCEPTS .................................................................................................................... 137

C SIMULATION MODEL INPUT DATA ............................................................................................. 141

D SIMULATION MODEL VERIFICATION .......................................................................................... 145

E WAITING TIMES CORRELATION GIVEN THE CAUSE OF DELAY ................................................... 147

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LIST OF FIGURES

Figure 1-1 The Port of Jebel Dhanna/Ruwais location ............................................................................3

Figure 1-2 Block diagram of report structure and relation between chapters. ......................................7

Figure 2-1 A typical port planning sequence (adapted from UNCTAD (1985)) – time not to scale. .......9

Figure 3-1 Approach channels of The Port of Jebel Dhanna/Ruwais (Admiralty chart No3179) and density map (MarineTraffic, 2014) . ..................................................................................................... 16

Figure 3-2 How to study a system. ....................................................................................................... 17

Figure 3-3 Simulation model classification. .......................................................................................... 18

Figure 3-4 Navigational services boundaries........................................................................................ 19

Figure 3-5 Main processes of the navigational services to vessels in a port with two intermediate anchorages. .......................................................................................................................................... 20

Figure 3-6 Timeline of processes. ......................................................................................................... 20

Figure 3-7 Scheme of The Port of Jebel Dhanna/Ruwais nautical services system. ............................ 21

Figure 3-8 Possible routes to enter the port. ....................................................................................... 22

Figure 3-9 Possible routes from outer anchorage to the inner anchorage. ......................................... 22

Figure 3-10 Flowchart of The Port of Jebel Dhanna/Ruwais processes. .............................................. 24

Figure 3-11 Reservation of one-way section for outgoing vessels. ...................................................... 26

Figure 3-12 Minimum required tide definition (not to scale). ............................................................. 27

Figure 3-13 Tidal window verification and route reservation. ............................................................. 28

Figure 4-1 Flowchart of clearance decisions performed by the simulation model. ............................. 45

Figure 4-2 One-way sections and corresponding traffic density map (MarineTraffic, 2014). ............. 46

Figure 4-3 Simulation model view. ....................................................................................................... 50

Figure 4-4 Terminals view. ................................................................................................................... 50

Figure 5-1 Cumulative distribution of total turnaround time. ............................................................. 54

Figure 5-2 Histogram of total turnaround time. .................................................................................. 54

Figure 5-3 Cumulative distribution of marine operations turnaround time. ....................................... 55

Figure 5-4 Histogram of marine operations turnaround time. ............................................................ 55

Figure 5-5 Cumulative distribution of total waiting time. .................................................................... 56

Figure 5-6 Histogram of total waiting time for all vessels. ................................................................... 56

Figure 5-7 Cumulative distribution of marine operations waiting time............................................... 57

Figure 5-8 Histogram of marine operations waiting time for all vessels. ............................................ 57

Figure 5-9 Average waiting times per cause of delay. ......................................................................... 58

Figure 5-10 Average waiting times per cause of delay – marine operations only. .............................. 59

Figure 5-11 Histogram of number of vessels at the outer anchorage. ................................................ 59

Figure 5-12 Histogram of number of vessels at the inner anchorage. ................................................. 60

Figure 5-13 Histogram of number of pilots. ......................................................................................... 60

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Figure 5-14 Histogram of number of tugs. ........................................................................................... 61

Figure 5-15 Fourty replications plot. Total vessels arrivals. ................................................................. 64

Figure 6-1 Incoming and outgoing occupation of S6 – timeseries. ...................................................... 66

Figure 6-2 Spacing verification for incoming vessels at the entrance of S5. ........................................ 67

Figure 6-3 Spacing verification for outgoing vessels at the entrance of S6/S7. ................................... 67

Figure 6-4 Tidal window verification – 6 month. ................................................................................. 68

Figure 6-5 Tidal window verification – 28 days. ................................................................................... 69

Figure 6-6 Port of Jebel Dhanna/Ruwais Limits (ADNOC - PPA, 2013). ................................................ 70

Figure 6-7 Histogram of simulated turnaround time, whithin port limits. .......................................... 71

Figure 6-8 Histogram differences (Simulated – Sea-web). ................................................................... 71

Figure 6-9 Cumulative distributions of simulated and Sea-web data. ................................................. 72

Figure 6-10 Histogram of service time as a deterministic input (Scenario 1). ..................................... 72

Figure 7-1 Histograms of total and marine operations waiting times. ................................................ 76

Figure 7-2 All causes and marine operations only waiting time box plots. ......................................... 77

Figure 7-3 Traffic and spacing (incoming vessels) waiting time box plots. .......................................... 78

Figure 7-4 Berth unavailability, spacing (outgoing vessels) and tidal-window waiting time box plots.78

Figure 7-5 Average waiting time per cause of delay. ........................................................................... 79

Figure 7-6 Average value and proportions of waiting time per cause of delay – Marine Operations. 80

Figure 7-7 Cumulative distribution of vessels at the Outer Anchorage. .............................................. 82

Figure 7-8 Cumulative distribution of vessels at the Inner Anchorage. ............................................... 82

Figure 7-9 Histogram of number of vessels at the Outer Anchorage. ................................................. 83

Figure 7-10 Histogram of number of vessels at the Inner Anchorage. ................................................ 83

Figure 7-11 Number of vessels at the anchorages due to marine operations sources of delay. ......... 84

Figure 7-12 Number of vessels at the anchorage due to berth unavalability. ..................................... 84

Figure 7-13 Marine operations turnaround time and service time. .................................................... 86

Figure 7-14 Waiting time over turnaround time for marine operations only. ..................................... 86

Figure 7-15 Berth unavailability waiting time over service time. ........................................................ 87

Figure 7-16 Waiting time over turanround time for marine operations only per cause of delay. ...... 87

Figure 7-17 Cumulative distribution of number of pilots. .................................................................... 89

Figure 7-18 Histogram of number of pilots. ......................................................................................... 89

Figure 7-19 Cumulative distribution of number of tugs. ...................................................................... 90

Figure 7-20 Histogram of number of tugs. ........................................................................................... 90

Figure 8-1 Utilization one-way sections – incoming vessels. ............................................................... 94

Figure 8-2 Utilization one-way sections – outgoing vessels. ................................................................ 94

Figure 8-3 Tidal window example of short open period and long closed period. ................................ 95

Figure 8-4 Utilization of the tidal window – tidal bounded vessels. .................................................... 96

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Figure 8-5 Histogram of duration of closed tidal window. ................................................................... 96

Figure 9-1 Berth occupancy in 2030. .................................................................................................... 99

Figure 9-2 Fleet mix for the hypothetical traffic increase. ................................................................. 101

Figure 9-3 Histograms of total and marine operations waiting times – Hypothetical Traffic Increase. ............................................................................................................................................................ 101

Figure 9-4 Total and marine operations only waiting times. ............................................................. 102

Figure 9-5 Average waiting time per cause of delay. ......................................................................... 103

Figure 9-6 Average waiting time per cause of delay – marine operations only. ................................ 104

Figure 9-7 Waiting time at the outer anchorage per cause of delay. ................................................ 104

Figure 9-8 Waiting time at the inner anchorage per cause of delay. ................................................. 105

Figure 9-9 Cumulative distribution of vessels in the outer anchorage. ............................................. 105

Figure 9-10 Cumulative distribution of vessels in the inner anchorage. ............................................ 106

Figure 9-11 Turnaround time and service time. ................................................................................. 107

Figure 9-12 Waiting time over turnaround time (marine operations). .............................................. 107

Figure 9-13 Waiting time over turnaround time per cause of delay (marine operations). ............... 108

Figure 10-1 Histograms of total and marine operations waiting times – Measures to Reduce Congestion. ......................................................................................................................................... 113

Figure 10-2 All causes and marine operation related waiting times. ................................................ 114

Figure 10-3 Average waiting time per cause of delay. ....................................................................... 115

Figure 10-4 Waiting time at the outer anchorage. ............................................................................. 116

Figure 10-5 Waiting time at the inner anchorage. ............................................................................. 116

Figure 10-6 Average waiting time per cause of delay- marine operations. ....................................... 117

Figure 10-7 Number of vessels at the Inner Anchorage. .................................................................... 117

Figure 10-8 Number of vessels at the Outer Anchorage. ................................................................... 118

Figure 10-9 Turnaround time comparisons. ....................................................................................... 119

Figure 10-10 Waiting time over turnaround time (marine operations). ............................................ 119

Figure 10-11 Waiting time over turnaround time per cause of delay (marine operations). ............. 120

APPENDICES

Figure A-1 Arrival process of a Tanker with no delays (MarineTraffic, 2014). ................................... 133

Figure A-2 Arrival process of a Tanker with delay (MarineTraffic, 2014). ......................................... 134

Figure A-3 Vessel shifting from outer anchorage to berth. ................................................................ 134

Figure A-4 Shifting of a cargo vessel from the outer to the inner anchorage. ................................... 135

Figure A-5 Vessel shifting from berth to inner anchorage. ................................................................ 135

Figure A-6 Traffic rules at one-way sections. ..................................................................................... 136

Figure B-1 Example of output and input Ports connections. ............................................................. 137

Figure B-2 Processor simplified order of events. ............................................................................... 138

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Figure B-3 Example of the use of triggers and messages. .................................................................. 138

Figure C-1 Definition of routes and limit speeds. ............................................................................... 141

Figure C-2 Relative number of vessels per class. ............................................................................... 143

Figure E-1 Correlation between waiting times given their cause from year 2014 to 2030. .............. 147

Figure E-2 Correlation between waiting times given their cause for the Hypothetical Traffic Increase scenarios. ............................................................................................................................................ 148

Figure E-3 Correlation between waiting times given their cause for the Measures to Reduce Congestion. ......................................................................................................................................... 149

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LIST OF TABLES

Table 2-1 Literature review comparison table. .................................................................................... 13

Table 2-2 Percentage of each model output in the literature review references. .............................. 14

Table 3-1 Relation between tasks supported by model results, model outputs and KPIs. .................. 38

Table 4-1 Jebel Dhanna / Ruwais Port destinations. ............................................................................ 43

Table 4-2 Fleet composition. ............................................................................................................... 43

Table 4-3 Description of the initial vessel labels. ................................................................................. 44

Table 4-4 Vessel class related speed. ................................................................................................... 49

Table 5-1 Traffic forecast. ..................................................................................................................... 51

Table 5-2 Number of berths at the end of each phase of the ChemaWEyaat terminal construction. 51

Table 5-3 Control variable and possible values to be taken. ............................................................... 52

Table 5-4 Sensitivity analysis scenarios. ............................................................................................... 53

Table 5-5 Scenarios definition .............................................................................................................. 62

Table 5-6 Variables considered for the number of runs definition. ..................................................... 63

Table 5-7 Total vessels that leaved the port – 40 replications data summary. ................................... 64

Table 7-1 Percentage of delayed vessels and average total waiting times for delayed vessels. ......... 76

Table 7-2 Percentage of delayed vessels and waiting times per cause of delay. ................................ 77

Table 7-3 Percentage of tidal bounded vessels and classes distribution. ............................................ 79

Table 7-4 Terminal occupancy rates. ................................................................................................... 81

Table 7-5 Average turnaround time, service time and average waiting time over turnaround time. 85

Table 7-6 Average KPIs per cause of delay. .......................................................................................... 85

Table 7-7 Number of served vessels and respective cargo throughput............................................... 88

Table 9-1 Scenarios definition ............................................................................................................ 100

Table 9-2 Hypothetical traffic forecast. .............................................................................................. 100

Table 9-3 Percentage of delayed vessels and average total waiting times. ....................................... 102

Table 9-4 Percentage of delayed vessels and waiting times per cause of delay. .............................. 103

Table 9-5 Average turnaround time, service time and average waiting time over turnaround time.106

Table 9-6 Average KPIs per cause of delay. ........................................................................................ 108

Table 10-3 Measures description. ...................................................................................................... 112

Table 10-2 Measures definition ......................................................................................................... 113

Table 10-4 Percentage of delayed vessels and average total waiting times. ..................................... 114

Table 10-5 Percentage of delayed vessels and waiting times per cause of delay. ............................ 115

Table 10-6 Average turnaround time, service time and average waiting time over turnaround time. ............................................................................................................................................................ 118

Table 10-7 Average KPIs per cause of delay. ...................................................................................... 120

Table 11-1 Evaluation scale. ............................................................................................................... 123

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Table 11-2 Evaluation results. ............................................................................................................ 123

APPENDICES

Table C-1 Inter-arrival times in minutes – IAT Global Table. .............................................................. 142

Table C-2 Example of distribution of vessels classes per destination– Traffic_2030 Global Table. ... 142

Table C-3 Minimum required tide and channel selection for each vessel class. ............................... 142

Table C-4 Initial label values for all vessel classes. ............................................................................. 143

Table C-5 Berth productivities. ........................................................................................................... 144

Table D-1 All Vessels Data output table ............................................................................................. 145

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LIST OF ABBREVIATIONS

ADCO The Abu Dhabi Company for Onshore Oil Operations ADNOC The Abu Dhabi National Oil Company AIS Automatic Identification System CD Chart Datum FERTIL Ruwais Fertilizer Industries GASCO Abu Dhabi Gas Industries KPI Key Performance Indicator MLLW Mean Lower Low Water MSL Mean Sea Level NED Negative Exponential Distribution SLAM Simulation Language for Alternative Modeling TAKREER Abu Dhabi Oil Refining Company UAE United Arab Emirates UKC Under Keel Clearance

LIST OF SYMBOLS

Parameter

Description

Dimension

b Berthing [T] BO Berth occupancy % d Accuracy [-] DWT Deadweight tonnage [M] et Extra time [T] KPI1 Rate of waiting w.r.t. the turnaround time % KPI1𝑀𝑂 Rate of waiting w.r.t. the turnaround time marine operations only % KPI2 Rate of waiting w.r.t. the service time % N Number of runs [-] P Productivity [M/T] pl Percentage of the total load to be handled % st Service time [T] st0 Service time when servicetimetype is 0 [T] st1 Service time when servicetimetype is 1 [T] st3 Service time when servicetimetype is 3 [T] TA Total time available [T] Tb Total time at berth [T] TR Turnaround time [T] TRMO Marine operations turnaround time [T] WT Total waiting time [T] wt Waiting time at every anchorage [T] WTb Waiting time due to berth unavailability [T] WTMO Waiting time due to marine operations [T] Zα/2 Two-tailed Z-score for a level of confidence 1-α [-] σ Standard deviation [-]

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1 INTRODUCTION

The assessment of port marine operations performance by means of simulation is treated in this master thesis project. An introduction to the graduation work is given in this chapter. In the first paragraph, background information is provided to enlighten the relevance of port marine operations performance assessment. The second paragraph gives a short presentation on this master thesis case study (further details are presented in chapter 3). The thesis objectives and research questions are presented in paragraph 1.4 followed by the methodology to achieve them. Finally, the last paragraph of this chapter consists of the outline of this report.

Background information 1.1

The primary functions of a port are traffic and transport, provided that ports are nodal points in the traffic connecting transport modes and turntables for various cargo flows (Ligteringen & Velsink, 2012). The interaction between the operations and services and the movement of cargo and vessels through a port makes evident the complexity of a port. Planning and designing such an infrastructure requires expertise in many disciplines such as transport economics, shipping, nautical matters, safety and logistics. In order to support alternative selection and assess the feasibility of a project, economic and financial analyses have to be performed.

The economic return on investment is the difference between benefits and costs (not only for the project owner but also for other participants in the trade). Given the ports diversity in forms of organization (level of privatization), it is impossible to generalize the costs, revenues and profitability. However, most world ports operate more or less on a commercial basis and are supposed to show a profit at the end of the year. The feasibility of a project under this circumstance is based on the ability of the port to make profit and to amortize investments.

Revenues usually come from tariffs accrued from various port activities which can be divided mainly in vessel-related and cargo-related services. In many cases, the port authority provides the vessel-related services and collects the associated fees. Revenues are related to the number of calls at the port and to the amount of cargo transported. Incomes from cargo-related services depend on the port operation type. When considering a service port, charges are collected directly by the port authority. In case of a land lord administrative model with a private sector terminal service provider, the port authority collects an annual fixed fee and a volume related variable fee.

Many other combinations of service providers and collection of charges are possible. However, for all port organization types, the number of served vessels and the cargo throughput of the project alternatives have to be determined in order to assess the revenues and consequently the feasibility.

When several commercial ports are sharing the same hinterland, tariffs are usually not the determining factor for attracting and preserving customers. If the distance between competitor ports is not significant, tariffs can be quite similar, given that costs do not vary significantly at close locations (Francou, 2010). Consequently, the quality of services becomes a more relevant element in this situation since it can affect customers’ choice. Therefore, in order to ensure the forecasted revenues, costumers’ requirements have to be met.

The quality of the services, the number of served vessels and the cargo throughput are therefore important factors related to the performance of the port which have to be evaluated during a feasibility study. This evaluation is usually supported by performance indicators which are used as a measure of performance. Assessing performance indicators is also crucial for port planning. Keeping track of indicators can enable timely decisions to improve productivity, improve service levels and influence investment decisions.

An introduction to port marine operations performance calculation is now given.

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Port marine operations performance calculation 1.2

Performance calculations are used in new port development projects to provide information to the financial and economic analysis and support evaluation of alternatives. For each designed alternative, the capability of handling the forecasted traffic has to be verified and the corresponding service level has to be assessed.

In the case of an existing port expansion project, performance calculations are executed for the current situation with the actual and forecasted traffic. If the assessed performance is below the acceptable service level or if the existing port is not capable of handling the forecasted traffic, bottlenecks may be identified and measures to remove them may be proposed.

The marine operations performance is treated during this graduation work. All navigational services provided to vessels from their arrival to the berthing operations and from unberthing to vessels departure are investigated and evaluated. The physical boundary on the land side is the berth. The cargo handling services and terminal processes are incorporated at high abstract level.

Three methods for assessing port performance are available (rules of thumb, queuing theory and computer simulation). Given the complexity of the port system, rules of thumb usually cannot be applied.

Currently, many consultancy companies estimate the port performance with labor intensive spreadsheets combined with engineering judgment. However, the outcome of such a study is a numerical answer only and due to extensive simplifications, the accuracy of the results is questionable and difficult to measure. Those types of calculations do not offer a wide range of results options and also this usually black box sort of calculations do not allow for a deeper understanding of the system.

Queuing theory is applicable for very simple systems for which environmental conditions, tidal windows, traffic rules and other functionalities are not considered.

Therefore, the use of simulation models is advised when more sophisticated results than the waiting time are required, for instance when the cause of delay (bottleneck) has to be identified. Hence, in many cases, using simulation models at the planning stages of port projects seems to be a logical decision, since it can provide more information for assisting the selection of alternatives and also for identifying possibilities for improvements in performance. By simulating an actual or a forecasted situation, either the most favorable port layout and operation or the most efficient utilization of existing facilities can be established (Groenveld, 2001).

An example of application of such simulation models is given by the case study accomplished during this thesis project. An introduction for the case study is given in the next paragraph.

Case study – The Port of Jebel Dhanna/Ruwais 1.3

The United Arab Emirates (UAE) is situated along the south-eastern tip of the Arabian Peninsula. The UAE has 700 kilometers along the Persian Gulf and 100 kilometers bordering the Gulf of Oman. The onshore terminals of the Petroleum Ports of the Abu Dhabi National Oil Company (ADNOC) are located on the Persian Gulf at a distance of about 220 kilometers West of Abu Dhabi. The location of the six major onshore terminals of the Port of Jebel Dhanna/Ruwais is presented in Figure 1-1.

The traffic flow of vessels into the Petroleum Ports of ADNOC has steadily increased during the period 2009-2012, 22% in total average. Over the next 25 years the same growth tendency is expected and new terminals are planned in order to cope with this progress. However, the expansions in port terminals may also give rise to concern when considering the port wet surface.

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Figure 1-1 The Port of Jebel Dhanna/Ruwais location

The approach channels to the Port of Jebel Dhanna/Ruwais are relatively long and occasionally limited in width and depth. There are sections in the channel restricted to one-way traffic and the channel depth imposes tidal limits to deep draught vessels. As the number of terminals in the region is increasing, it is possible that further developments of the port will result in congestion and that the capacity of the channels to the open sea will become a limiting factor.

The aim of the case study is to analyze the current and future marine operations performance of the Port of Jebel Dhanna/Ruwais by implementing a simulation model dedicated to it.

Chapter 3 gives an extensive description of the marine operations processes of the Port of Jebel Dhanna/Ruwais to be simulated.

The simulation software to be used is FlexSim. It is a very versatile modeling tool developed by FlexSim Simulation Products. FlexSim is a 3D, object-oriented discrete event modeling tool, developed to simulate logistic processes. Applications of similar softwares to ports approach channels are identified in the literature review; however, none of them have made use of FlexSim.

FlexSim was identified by a previous intern at Royal HaskoningDHV to be suitable for performing port studies such as channel capacity assessments (van Heemst, 2013). The suitability to apply FlexSim for performing the case study is verified during this graduation work and the software application is evaluated.

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Thesis objective 1.4

This section is dedicated to the definition of the Master Thesis objective and research questions.

Primary objective

The objective of this thesis is to assess, by using a simulation model, the current and future performance of The Port of Jebel Dhanna/Ruwais marine operations, in order to evaluate the congestion development and support decision making.

Research questions

During the elaboration of the research proposal, a lack of detailed information on the use of simulation models for assessing marine operations performance was identified. Plenty of generic information on simulation is available; however, when it comes to this more specific assignment, mainly case studies were encountered, with the central focus on the results of the simulations and not in the entire process of implementing a marine operations simulation model.

This graduation work also consists of a case study; however, in order to contribute to science and practice the following research questions were identified as relevant.

Which processes are necessary to be represented in detail to assess the Port of Jebel Dhanna/Ruwais marine operations performance in the context of a feasibility study?

What is the level of detail required for the input parameters to represent the Port of Jebel Dhanna/Ruwais real situation?

Which are the Key Performance Indicators for Port Marine Operations that have to be assessed for this case study accomplishment?

How is the performance of the Port of Jebel Dhanna/Ruwais marine operations for the actual and forecasted traffic and which are the bottlenecks?

Which are the most effective measures to solve the bottlenecks and improve the Port of Jebel Dhanna/Ruwais performance?

Which is the added value of performing simulations in order to assess marine operations performance compared to hand calculations?

Is the FlexSim software an appropriate tool for conducting marine operations performance assessments?

Contribution to science and practice

By answering the above mentioned research questions it is expected that more detailed information concerning the assessment of marine operations performance assisted by simulation models will be available in literature. Although the answers are applicable for this specific case study, whenever possible during this report discussions on a more general level are presented, as it is the case for the level of detail of input parameters.

Additionally, no previous use of FlexSim for performing such assignments is available in literature. Therefore, the evaluation of the applicability of this software for assessing the performance of port marine operations is a contribution to practice.

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Research approach 1.5

The phases to be followed during the course of this master thesis are briefly described in this section.

Literature review

A literature review on port planning stages and the use of simulation models for ports systems is performed. This section gives support to the following steps (verbal and simulation models).

Verbal model

The definition of the problem to be solved, the system itself and the system boundaries are further elaborated followed by a complete description of the processes of the port marine operations and respective flow chart. The detailed and simplified processes to be implemented in the simulation model in order to perform the case study are selected and the model functionalities are established. A discussion on the parameters needed to represent each process as well as the level of detail required to correctly reproduce the real life system is given. The key performance indicators related to port marine operations to be assessed for the case study are selected.

Simulation of current operation

An introduction of the FlexSim objects used to translate the verbal model into a simulation model is given. The model is implemented based on the current operational situation of the Port of Jebel Dhanna/Ruwais. The navigational service system including the Port Regulations and limitations with respect to maximum draught, speed, options for one/two way traffic are included in the model. Additionally, a sensitivity analysis is accomplished in order to identify the relevance of parameters and the influence of the level of detail of input parameters previously selected. The implemented processes and functionalities are then verified. A validation of the model is also performed (by comparing the results to AIS data). Then, forecasted traffics are simulated and the model outputs are streamlined based on the results requirement.

Assessment of the current marine operations performance

Results of the model are used to evaluate the performance of the marine operations of the port for the actual and forecasted traffic. The development of congestion is monitored and a detailed description of the impact of the future growth is given, such as: potential congestion of the anchorage areas and channels; potential overload of port services and potential delays.

The constraints are listed and solutions that can reduce the congestion with respect to port marine operations are proposed.

Hand calculations

The utilizations of the one-way sections of the channel and of the tidal window are approximated. The assumptions undertaken for this assessment are presented as well as the limitations of such approach. The results obtained by this simple method are compared to the ones given by the simulation model. The added value of simulations compared to the hand calculations is discussed.

Simulation of proposed measures

The new functionalities needed to assess the performance of the proposed alternatives are implemented and verified. Simulations are performed for each of the proposed measures.

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Assessment of the proposed measures performance

The performance and the effectiveness of each measure is evaluated and discussions to support the most feasible option are conducted.

Discussion on the simulations added value and evaluation of the FlexSim tool

An analysis of the added value of the simulations compared to the hand calculations as well as an evaluation of the FlexSim model applicability for the case study is conducted.

The criteria to evaluate the FlexSim tool are:

FlexSim should be able to reproduce all marine operations. (Adequacy) Adding new functionalities, other than the default ones, should not be significantly

time/effort demanding. (Adaptability) Changes in the model, for instance when simulating various alternatives, should be easily

implemented. (Flexibility) The program should not crash frequently while programming or simulating. (Stability) The simulation time should not be a limiting factor. The time that it takes for the model to

run should be adequate to performing feasibility studies. (Simulation time) The output should be easily obtained from the model. Client-friendly results directly attained

by the model, without too much post processing, are appreciated. (Quality of model output)

Conclusions and recommendations

The conclusions are formulated with direct connection to the research questions. The key findings and the contribution to existing knowledge is given. Discussions on the results and recommendations for further design or research are also presented.

Report structure 1.6

The structure of the report results from the research phases presented in the previous paragraph. In Chapter 2 a literature review is given. The importance of performance calculations at port planning stages is highlighted and support is given to the selection of simulation models for executing this task. Questions concerning the simulation model requirements at port planning stages are raised. A list of previous studies in this subject is provided and the existing models are compared to the one of this master thesis in terms of model output and boundary conditions.

Chapter 3 expounds the schematization of the reality to be simulated (verbal model). The problem to be solved is defined as well as the system and its boundaries. The Port of Jebel Dhanna/Ruwais navigational services are extensively described and the processes to be simulated, the model requirements and assumptions are given.

Chapter 4 contains all information about the implementation of the simulation model. The FlexSim objects and their application are introduced. Finally the model is presented.

The experiments to be performed with the model are then described in Chapter 5. The traffic forecast and the control variables that compose every scenario are discussed. The sensitivity analysis which is performed in order to define the combination of control variables is presented as well as the scenarios to be simulated. Practical issues of the simulations such as the definition of the simulation duration, the warmup time and the number of repetitions are discussed.

In Chapter 6 the model is verified and validated. Chapter 7 contains all model results and discussion related to them. The performance of the Port of Jebel Dhanna/Ruwais marine operations is evaluated. Chapter 8 gives a comparison between the simulation model results and simple hand

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calculation that can be used to estimate the level of congestion of a port. A discussion on the added value of performing simulation is given. Chapters 9 and 10 introduce additional simulations that are performed and discussions on the obtained results. Chapter 11 evaluates the use of FlexSim in this MSc Thesis.

Chapter 12 expounds the conclusion achieved during this graduation work as well as the answers to the research questions and the recommendations for further research. Finally, a list of references is given in Chapter 13 and the appendices are provided.

The relation between chapters is presented in the block diagram of Figure 1-2.

Figure 1-2 Block diagram of report structure and relation between chapters.

Research background Implementing, testing and validating the simulation model

Simulation model application and

assessment of the marine operations

performance

Conceptual foundations of the model

Reasearch motivation and contributions

2. LITERATURE REVIEW 3. VERBAL MODEL

4. SIMULATION MODEL

IMPLEMENTATION

5. SIMULATION MODEL

EXPERIMENTAL SETUP

6. SIMULATION MODEL

VERIFICATION AND VALIDATION

7. ASSESSMENT OF THE MARINE OPERATION

PERFORMANCE

8. COMPARISON TO HAND

CALCULATIONS

9. HYPOTHETICAL TRAFFIC INCREASE

10. MEASURES TO REDUCE

CONGESTION

1. INTRODUCTION

12. CONCLUSIONS AND

RECOMMENDATIONS

11. EVALUATION OF THE USE OF FLEXSIM

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2 LITERATURE REVIEW

In this chapter a literature review on port planning stages and the use of simulation models for port systems is presented. The decision of carrying out simulations for calculating the performance at port planning stages is supported and models from literature are listed and compared to the one implemented during this graduation work.

Port planning stages 2.1

The development of a port consists of a combination of medium-term and long-term planning of new facilities. In the case of an existing port (which is the case study situation), it also includes a program of short-term actions to improve the present facilities and their use.

A typical sequence of tasks to be accomplished during the port planning is presented in Figure 2-1. The project planning usually accounts with a feasibility study which is highlighted in the yellow box.

Figure 2-1 A typical port planning sequence (adapted from UNCTAD (1985)) – time not to scale.

The steps of the port project planning concerned in this graduation work are shown in red. It comprises the current performance analysis and proposal of alternatives for removing bottlenecks followed by the assignment of an operational plan for each alternative. The assessment of the respective performances to provide information for feasibility analysis and subsequent filtering-out of alternatives is also considered (what level of service is reached by each combination of traffic and facilities).

It was concluded at the introduction section that the use of simulation models for assessing the performance is advised when the port to be studied cannot be modeled by a simple system. For instance, when environmental conditions, tidal windows, traffic rules and other functionalities play a role, the queuing theory and simpler calculations are no longer applicable and simulations are required in order to include all processes involved.

Moreover, simulation is being required ever more by the consultant clients and there are many factors that can have contributed for this intensified demand. For instance, the fact that simple

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calculations may not offer the required range of results options and also their usually black box sort of calculations may not allow for a deeper understanding of the system. Additionally, another contribution can be due to the increase in complexity of port systems which makes it more and more difficult to achieve reliable results with simple calculations. The evolution of computers and simulation tools, which decreases the time needed for performing the simulations also have contributed to this demand increase. Section 2.2 provides a literature research on the use of simulation models for ports.

When the option for using simulation models to assess the performance at port planning stages is taken, some questions concerning the model requirements may arise:

1. What kind of information is usually available at port planning stages? Are the required inputs to the simulation model available at this project phase?

2. Which parts of the port should be included in the simulation model? (Section 3.3) 3. Which level of detail of the model input is required considering the limitation on information

and time? (Section 3.8) 4. Which are the results required from the simulation model (performance calculations)?

(Section 3.9)

Answers to the above mentioned question are not straightforward and are usually strongly dependent on the project type, for instance if it is a new port development or an existing port expansion.

Information available at port planning stages

The information required for executing performance simulations at port planning stages was identified as being:

Physical features: weather, tide; Traffic forecast; Port layout, bathymetry; Port operations.

The availability of information may differ in case of a greenfield port development or an existing port expansion. For instance, it is clear that for the latter situation, the access to physical features and operational planning information is easier than for the former situation.

In the case of a new port development usually less physical features information is available. However, if the project planning is being developed within the framework of a master plan, broad engineering surveys, such as hydrographic, topographic, oceanographic, hydraulic and geotechnical surveys should be available and complemented by specific site surveys when needed in order to increase the degree of detail and the reliability of data. In the case of an existing port expansion, due to the constant monitoring of physical features, the need of specific site surveys is only needed when the alternatives for sea approaches and locations for the port installations are outside the existing port boundaries.

Broad traffic forecasts should be available either for new ports or expansion of existing ones requiring only a review and update of data in both cases.

For every specified feasible option for which the performance should be evaluated a preliminary design is required. The layout should be designed in sufficient detail to ascertain access, operating and storage problems (UNCTAD, 1985). One should keep in mind that the traffic forecast and the physical features are essential information for other planning stage phases than the performance simulation and having access to it should not be a problem.

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Defining the operational planning of a port is easier when dealing with an extension project. However, for new port developments, the client should be able to specify all information needed for the simulations.

Whether the information available at port planning is enough for carrying out the simulations depends on the level of detail required for the input information. However, the port planner can require further information when needed and when that is not possible reasonable and experienced based assumptions should be made to counteract this unavailability of inputs.

The case study presented at this graduation work consists of a port expansion and at the input stage of the simulation model the available information and the assumptions made in case of absence of data are presented.

Simulation models for ports 2.2

The use of simulation models for design and planning of ports is getting ever more common. The applicability of simulation models in such a complex system is vast and tools are available for simulating, for instance:

Port’s logistic; Vessel navigation/manoeuvring ; Vessel operations; Hydrodynamic and sediment transport.

Logistic models can be used for assessing the performance of an existing or planned port for both actual and anticipated traffic. Evaluation of alternatives and identification of bottlenecks can be performed and an indication of the safety level can be given by registering the potential number of exposures. Navigation simulations are carried out in order to assess risk and safety for optimizing the channel and manoeuvring areas design. Mooring forces and vessel movements can be assessed by Dynamic Mooring Analysis modelling. Several software tools are available for simulating free surface water environments and perform hydrodynamic and sediment transport modelling in order to provide information to support design and environmental assessment.

In order to evaluate the marine operations performance with such a logistic simulation model can be used. Several simulations must be run for taking into account the stochasticity of the inputs and the simulation time should be long enough to give average conditions results. Therefore, a fast-time simulation is recommended. Fast-time simulation models can be run faster than reality due to the exclusion of human interaction. The speed of the simulation depends on the computer capacity, the programmer experience and the complexity and level of detail of the model.

Simulation in the maritime transportation domain has been used in many cases. On the other hand, literature on simulation modeling of vessel traffic on ports is not large but growing (Altiok, Almaz, & Ghafoori, 2012). Examples of application found at the literature are given below.

Literature models 2.2.1

A large-scale port simulation model was developed in 1974 as a joint project involving the United Nations Conference on Trade and Development (UNCTAD) and other institutions (Eidem, 1974). In the early 80’s, the application of a Simulation Language for Alternative Modeling (SLAM) model to the Suez Canal is reported (Clark, Kabil, & Mostafa, 1983). An experimental traffic control scheme is proposed, tested and discussed. A method for analysis of systems with multiple response variables is discussed and illustrated. At the same year, a tool for the design of a new port or marine terminal and the extension or improvement of an existing port (improvement of the nautical conditions, enlarging quay length, enlarging trans-shipment capacity, etc.) is presented: the Harborsim model (Groenveld, 1983a, 1983b).

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Park and Noh (1987) present a model that consists of two parts: a physical impact simulation, and an economic impact simulation. The first part of the model simulates the effects caused by the port capacity expansion. The second part evaluates the port economics due to changes in the port capacity. The operations covered by the model are: marine transport, cargo handling and inland transport being the focus of the just mentioned paper broader than the one of this master thesis.

A simulation model was set up by van de Ruit, van Schuylenberg, and Ottjes (1995) in order to investigate the potential flow of shipping traffic in the Maasvlakte and verify if the increased traffic due to new developments would lead to congestion in the water area. Weather conditions and tides were not considered in the simulations.

A detailed maritime traffic simulation model was developed by Thiers and Janssens (1998) for the port of Antwerp, Belgium including navigation rules, tides and lock operations in order to investigate effects of a container quay to be built outside the port on the vessel traffic and especially on the waiting time of the vessels. Demirci (2003) constructed a simulation model of the Trabzon Port in Turkey to analyze port operations and assist investment planning. The project was focused on the investigation of bottleneck points and the addition of new port equipment at these points. However, the model covered vessel and cargo handling and warehouse operations and included also inland transportation differently from the focus of this graduation work.

At Port of Seville - Spain, both freight traffic and terminal logistics were simulated focusing on port utilization. In a second moment a large amount of computational experiments were conducted in order to improve the berth management strategy (Arango, Cortés, Muñuzuri, & Onieva, 2011; Cortés, Muñuzuri, Nicolás Ibáñez, & Guadix, 2007).

Additionally, in various studies, vessel traffic simulation was used for further analysis of accident probabilities, risks and various economic and technical issues. A focus on ship behaviour and vessel manoeuvrability was identified when it comes to nautical traffic models. There are also several publications of simulation used as a tool for design and optimization of the access channel horizontal and vertical dimensions as in: (Quy, Vrijling, & van Gelder, 2008; Rayo, 2013; Siregar, 1995).

When it comes to the scope of this graduation work only few similar publications were found. Two applications are of relevance, one for the Delaware River in the USA and other for the Port of Santos in Brazil.

From 2007 to 2012 a project entitled “Modeling and Analysis of Maritime Traffic in Delaware River” was held. A 30-year planning horizon was used in the project at which a detailed large-scale simulation model was developed and used for the analysis of impact of deepening on port performance. A risk analysis was accomplished and vessel prioritization policies during port reopening (after a closed period due to oil spills) were studied (Almaz & Altiok, 2012; Altiok et al., 2012). The focus of the project in the Delaware River, similarly to this graduation work, was from vessel arrival to the berth including only rough simplifications of the terminal operations in the form of a part of the vessel holding time which is the total time spent by a vessel in the terminal.

For Port of Santos – Brazil, a simulation model was used to evaluate the impact of the access channel navigational operation policies on the port efficiency (Mota, Pereira, & Botter, 2013). Additionally, the model supported the design of new terminals operations for Santos, delivering KPIs such as: canal utilization, queue time, berth utilization, and throughput capability. Further information on the concept used for performing the simulation is given in Mota, Pereira, Botter, and Medina (2013).

The requirements of the simulation model necessary to accomplish this case study are now compared to the simulation models existing in literature.

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Comparison with literature models 2.2.2

A summary of the most relevant references of this literature review is presented in Table 2-1. For each reference, the model outputs are compared to the ones required for this graduation work case study. The derivation of the following list is presented in paragraph 3.9 however, for comparison purposes this information is here forestalled.

The model outputs indicated at the table are:

1. Total waiting time 2. Waiting times per location 3. Waiting times per cause of delay 4. Number of vessels in the queue 5. Berth occupancy 6. Turnaround time 7. Service time 8. Number of served vessels 9. Cargo throughput

All those outputs are to be delivered by this case study simulation model, therefore all model output cells of the table are filled for this graduation work model. The corresponding simulation tool to be used is FlexSim.

Grey filled reference cells characterize simulation models with similar boundaries (from vessel arrival to the berth and vessel departure, with the quay operations as a boundary condition). References in bold contain simulation models with the most similarities to the one of this graduation work.

Table 2-1 Literature review comparison table.

REFERENCE MODEL OUTPUT SIMULATION 1 2 3 4 5 6 7 8 9 LANGUAGE TOOL

Altiok, Almaz, & Ghafoori, 2012 Arena Arango, Cortés, Muñuzuri, & Onieva, 2011 Arena Clark, Kabil, & Mostafa, 1983 SLAM Cortés, Muñuzuri, Nicolás Ibáñez, & Guadix, 2007 Arena Demirci, 2003 AWESIM Groenveld, 1983a, 1983b Prosim Harborsim Mota, Pereira, & Botter, 2013 Arena Almaz & Altiok, 2012 Arena

Park & Noh, 1987 FORTRAN and

SLAM Rayo, 2013 Matlab Thiers & Janssens, 1998 SIMAN Arena van de Ruit, van Schuylenberg, & Ottjes, 1995 Prosim

Some of the reference models presented at the above table have other outputs than the listed ones. These additional model outputs are either related to safety (like number of incidents) or to processes which are outside this graduation work model boundaries (such as storage availability and other terminal related outputs). Not all functionalities and results of reference models are described.

The percentage of references which contains each of the model outputs compared to all references, or to only the ones with similar boundaries is shown on Table 2-2.

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Table 2-2 Percentage of each model output in the literature review references.

% MODEL OUTPUT

1 2 3 4 5 6 7 8 9 All comparable references 100 92 17 67 42 50 58 17 8

Similar Boundaries references 100 86 29 71 43 43 57 29 14

It can be noticed from the table that the total waiting time is the most common model output since it is recognized in all references (100%). The least common model outputs are: the waiting times per cause of delay, the number of served vessels and the cargo throughput.

A new simulation model is therefore built in order to deliver all required outputs.

Outside the academic community, it is not difficult to identify in companies’ brochures or portfolios the use of traffic simulation models for supporting port design and planning. Some examples of applications are:

the use of a vessel traffic simulation study to investigate the impact of an increase in calling vessels as a result of future port expansion;

assessment of port capacity and performance metrics for supporting feasibility studies; analysis on the effects of alternative port operating rules, berth configurations, channel

depth and duplication; optimization of investments and operating costs.

However, since those projects are not publically available, it is not possible to evaluate which is the best practice being used be the modellers.

Conclusions 2.3

From the literature review, it can be concluded that despite the fact that the use of logistic simulation models for navigational services is increasing, and many practical applications are identified, the number of publications and academic studies is still limited.

Amongst the listed literature models, a tendency of case studies is noticed, which is also valid for this Master Thesis. This fact gives an indication of the difficulty to generalize this subject. Whenever possible during this graduation work, discussions on a general level are given with the intention of making it the most applicable as possible to other studies with the same purposes.

Since no previous marine operations performance assessment using FlexSim as the simulation tool were found in literature, the application of FlexSim for this purpose is tested and evaluated.

Even though the use of simulation models is recommended when complex systems are to be simulated and further results than waiting times are required, it seems that this output is still the most popular one. The waiting time per cause of delay, which is an important output for understanding a complex port system, is between the most infrequent ones (present in 17% of the models considered in the literature review).

By describing extensively the methods used in this graduation work, it is expected that though a case study is analysed, the given research approach can be adapted and harnessed in other problems with similar aims.

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3 VERBAL MODEL

The implementation of a simulation model requires a prior description of the model by schematizing the reality within the system boundaries. This section is dedicated to situating the problem, defining the system and its boundaries, and performing the schematization of the real system. The latter is accomplished by defining and describing the processes to be simulated as well as the model requirements in terms of functionalities, level of detail of inputs, and results. In other words, the conceptual foundations of the model are given. Additionally, the model assumptions are listed.

Problem definition 3.1

The Port of Jebel Dhanna/Ruwais consists of six major terminals. The crude oil terminal at Jebel Dhanna is operated by The Abu Dhabi Company for Onshore Oil Operations (ADCO). The other five terminals at Ruwais are a refined oil terminal operated by Abu Dhabi Oil Refining Company (TAKREER), a gas terminal operated by Abu Dhabi Gas Industries Ltd. (GASCO), a bulk cargo Urea and Liquid Ammonia terminal operated by Ruwais Fertilizer Industries (FERTIL), a Sulphur Handling Terminal operated by GASCO, and a Polyethylene terminal operated by the Abu Dhabi Polymers Company Ltd. (BOROUGE). In 2016, phase one of the ChemaWEyaat terminal will start operating with three new berths and after phase 12 (planned for 2030) 18 new berths in total will be in commission.

The configuration of the port network of access channels is presented in Figure 3-1. Vessels arrive from the upper right corner and berths are represented by the colored symbols at the figure bottom center. Three waiting areas are present in different sectors of the port: outside port, outer anchorage and inner anchorage.

The North Channel and the Relief Route can only be used by vessels with a Pilotage Exemption Certificate when entering or leaving the port. However, since this certificate can only be given to vessels with limited size (3000 NRT) most vessels follow the main access channel route as it can be seen on Figure 3-1 (bottom left). All vessel positions recorded by Marine Traffic during the last semester of 2013 are included in this figure. Hot colors indicate high density; however, the exact color scale is not available.

A number of expansions and new projects are currently under development within and adjacent to the above established terminals. A forecasted traffic and throughput is related to the existing and the new terminals. The feasibility of the new projects is based on these data.

The traffic demand of the new developments will share the present nautical infrastructure with the current traffic. The access channels ability of coping with the forecasted traffic has to be assessed. In case of unacceptable service levels or when the forecasted traffic cannot be handled, bottlenecks should be identified and removed in order to adapt the capacity of the navigational services to the terminals demand. The number of pilots and tugs required for assisting the calling vessels is also a point of concern.

The tasks to be performed by the port planner in order to solve the problem are summarized at the following list:

To assess the ability of the port to handle the forecasted traffic and throughput; To investigate the development of congestion with increasing traffic; To assess the performance of the port with increasing traffic; To identify bottlenecks (sources of delay); Estimate the number of pilots and tugs needed to provide the services.

A simulation model is implemented during this graduation work for assisting the solution of this problem. In order to do so, the implemented simulation model has to be able to reproduce the Port of Jebel Dhanna/Ruwais navigational service system.

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Figure 3-1 Approach channels of The Port of Jebel Dhanna/Ruwais (Admiralty chart No3179) and density map

(MarineTraffic, 2014) .

The following sections present first, the definition of the system, then the system boundaries and the model requirements for functionalities and input. Subsequently, the model requirements are described.

YABR CHANNEL

RELIE

F ROUTE

NO

RTH

CHAN

NEL

MAIN ACCESS CHANNEL

DW CHANNEL

STEWART C.

GHASHA BUOY

OUTERANCHORAGE

QUEUE OUTSIDE

PORT

INNERANCHORAGE

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System definition 3.2

The system to be simulated is a service system. Service is a “social act which takes place in direct contact between the customer and representatives of the service company” (Normann, 1984). Service systems provide one or more services to flowing entities (for example, customers) through system resources and operations. Entities are routed through a sequence of processing operations at which system resources provide the required service (Al-Aomar, 2010).

Calling vessels are the customers which are served by the port. While “flowing” through the port the vessel is submitted to a sequence of operations provided by the port resources. When analyzing the port marine operations, for obvious reasons, it is just not feasible to use the real port navigational service system to perform experiments. Therefore, a model which represents the actual system is required. This is the case for most of the service problems which are usually related to the need of treating customers and their requests at the highest level of satisfaction for the lowest possible cost.

A model is a representation of the construction and working of some system of interest. A model is similar to but simpler than the system it represents. On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features. On the other hand, it should not be so complex that it is impossible to understand and experiment with it. A good model is a judicious tradeoff between realism and simplicity (Maria, 1997).

Several of the unique characteristics of service systems can be captured in a computer model that behaves almost similar to a real/world service system. Computer model simulations can be used to study the service system behavior, quantify the provided service, compare proposed alternatives for providing services, improve service level, better utilize resources, reduce service time and cost, and setup/configure the service system to provide the best performance possible within given constraints (Al-Aomar, 2010).

When assessing the performance of a service system a conceptual model is required and usually due to the stochastic processes involved and the complexity of the system many of the service problems are studied by performing simulations. Simulation is the process of running a (computer) model of a real system to study or conduct experiments. In its broadest sense, simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time. The possible ways of studying a system can be visualized in the tree diagram of Figure 3-2.

Figure 3-2 How to study a system.

System

Experiment with the actual system

Experiment with a model of the system

Experiment with a physical model

A conceptual model

Analytical solution

Simulation

17


FlexSim is used in this graduation project to perform the simulations. FlexSim is an object-oriented discrete-event simulation tool, which means that it is used to model systems whose changes in state occur at discrete points in time as a result of specific events (FlexSim Software Products Inc, 2014). Figure 3-3 presents the classification of simulation models and the discrete-event simulation which corresponds to the FlexSim classification is emphasized by the grey box (bottom right).

Figure 3-3 Simulation model classification.

One of the first steps that the simulation practitioner must perform in the system definition phase is to classify the system (Chung, 2004). The navigational service system has more than one random variable being classified as a stochastic system. The state of the system is dependent on time, thus it is a dynamic system. The changes in the system state occur only in some given instants of time, thus the system is defined as a discrete event system. Therefore, FlexSim is expected to be an appropriate tool to perform the simulation of the navigational service system. Additionally, the suitability of FlexSim to simulate the capacity of an approach channel of a maritime port was previously analyzed during an internship at RoyalHaskoningDHV with promising results (van Heemst, 2013).

It is important to highlight that a discrete representation is always affected by the loss of information. The choice of events that are considered relevant inevitably excludes other possible events. Therefore, the level of detail introduced by the simulation designer directly affects the precision loss (Sonnessa, 2004).

System boundaries 3.3

The boundaries of the navigational service system are from vessel arrival to the berth and from berth to vessel departure. All navigational services provided to vessels from their arrival to the berthing operations and from unberthing to vessels departure are investigated and evaluated (Figure 3-4).

Nevertheless, the physical boundary at the port side, which is represented by the berth, coincides with the quay operations process. For the situation of an existing port, the quay operations can be simplified and the berth productivities and other efficiency and effectiveness metrics are usually known. The level of detail of quay and terminal process is therefore simplified and the influence of it is investigated at the sensitivity analysis.

Moreover if the high berth occupancy is found to be the cause of congestion and the quay operations productivity is liable of being improved, the effect of such improvements should be tested (out of the scope of this MSc Thesis). If the results are promising for increasing port capacity, further

Simulation model

Deterministic

Stochastic

Static

Monte-Carlo simulation

Dynamic

Continuous

Discrete

Discrete-event simulation

Static

Dynamic

Continuous

Discrete

18


investigation of the quay and terminal operations should be recommended. Yet, for initial planning stages, quay and terminal operations can be rather simplified keeping the focus on the marine operations.

Figure 3-4 Navigational services boundaries

Description of navigational services on a high aggregation level 3.4

The main processes of the navigational services to vessels in a port with two intermediate anchorages are shown in the flowchart of Figure 3-5. The processes are represented on a rather high aggregation level. For instance, the boarding and disembarking of the pilot is not detailed. Tugs making fast and releasing the mooring lines and the turning manoeuvre (when applicable) are included in the sail to berth process. All speed variations are included in the sailing processes. Berthing accounts for the final stop and mooring. Unberthing considers also the tugs making fast and the unmooring procedure.

The cargo handling services at the quay include loading cargo on/off the vessel and in addition other operations such as: hose (dis)connection, (un)lashing, opening/closing holds. At this high aggregation level the quay operations represent the time at berth between berthing and unberthing including therefore also the paperwork. Additionally, the waiting time at the anchorages includes the sailing to and from them given that once the vessel leaves the original route (without delays) the waiting time at the anchorage starts to be counted.

The timeline of Figure 3-6 shows the processes in an even higher level of aggregation with the focus on defining the waiting times. Therefore, all other processes are aggregated into transit and service times. This timeline considers that the vessel is delayed at every possible waiting place. When the vessels is not delayed at all, every orange bar disappears.

The waiting time at the anchorages includes the sailing to and from them for the same reasons already presented for the flowchart. The waiting time at berth is the time between the end of the quay operations and the start of the unberthing procedure.

NAVIGATIONAL SERVICES SYSTEM BOUNDARIES

Approach channels

Anchorages

Pilotage, tugs and mooring gangs

Aids to navigation

Traffic control - VTMIS

Berth

Nav

igat

iona

l ser

vice

s to

VES

SELS

VESSEL ARRIVAL VESSEL DEPARTURE

Cargo flowShip flow

CARGO Handling services

Marine operations

19


Figure 3-5 Main processes of the navigational services to vessels in a port with two intermediate anchorages.

Figure 3-6 Timeline of processes.

Vessel arrives

Permission to enter outer channel Wait

No

Sail through outer channel

Yes

Permission to enter Port

Wait at outer anchorage

No

Yes

Sail through inner channel

Permission to berth

Sail to berth

Yes

Berthing Unberthing

Sail out of berth

Wait at inner anchorage

No No

Sail through inner channel

Yes

Permission to enter outer channel

No

Sail through outer channel

Yes

Vessel leaves

MARINE OPERATIONS BOUNDARY

OUTER FAIRWAY

Permission to leave berth

Yes

Wait at Berth

No

Permission to leave Port

Quay operations

Berth

Turnaround timeOuter

Anchorage

Waiting time

End of quayoperations

Queueoutside Port

Vessel arrival at entrance buoy

InnerAnchorage

Vessel is berthed

Service time (including (un)loading, hose (dis)connection, (un)lashing, opening/closing holds, paperwork) Transit time (including pilot (un)boarding, tugs making/releasing fast and (un)berthing)

Vessel leaves The Port

Vessel leaves the berth

InnerAnchorage

Waiting places

20


The transit time is the time the vessel spends at the access channels which corresponds to the sailing time and includes, when applicable, pilot boarding and disembarking, tugs making fast and releasing and (un)berthing. The service time is equal to the time at berth minus the waiting time at berth. It goes from when the vessel is berthed until the end of the quay operations.

However, when it comes to The Port of Jebel Dhanna/Ruwais navigational services, the complexity of some processes is increased due to the network of channels enabling the navigation through different routes with the selection method based on the Port Regulations.

Detailed description of the Jebel Dhanna/Ruwais navigational 3.5services

This section describes the sequence of processes experienced by the vessels calling at the Port of Jebel Dhanna/Ruwais. First, the possible routes to be followed are presented since a different sequence of processes applies to distinct taken routes.

A scheme of the Jebel Dhanna/Ruwais channels network is presented in Figure 3-7.

Figure 3-7 Scheme of The Port of Jebel Dhanna/Ruwais nautical services system.

The channels are divided into sections (S1 to S12). The division is made based on the diversity of routes; every route is formed by the combination of different sections. Wherever a confluence or a bifurcation occur an edge of a section is created. Additionally, different least depths and operations (one- or two-way) are also distinguished.

S1S2

S3

S8

S9

S10

S4S5

S6

S7

S11

TERMINALS

VESSEL ARRIVAL

GHASHA BUOY

OUTERANCHORAGE

INNERANCHORAGE

QUEUE OUTSIDE

PORT

S12

S5a

21


Attention should be given to the fact that with the current Port Regulations only two routes should be used. The reasons for that are subsequently explained. However, the complete network of channels is described in order to include it in the model with the intention of identifying the effects of different operations on the port efficiency.

The possible routes to be followed by the vessels when getting at the port are (Figure 3-8):

a) S1 + S2 + S3 (Main Access Channel) - from outer fairway to the outer anchorage b) S1 + S2 + S9 + S10 (North Channel) - from outer fairway to the outer anchorage c) S1 + S2 + S9 + S11 + S12 (North + Yabr Channel) - from outer fairway to the inner anchorage d) S1 + S8 + S10 (Relief Route) - from outer fairway to the outer anchorage e) S1 + S8 + S11 + S12 (Relief + Yabr Channel) - from outer fairway to the inner anchorage

Figure 3-8 Possible routes to enter the port.

The current Port Regulations state that only masters of exempted vessels of suitable draught should use the North Channel (S9) or Relief Route (S8) when entering or leaving the port. Exempted vessels are the ones that retain a pilotage exemption certificate.

“Port Pilotage exemption certificate may be granted to Masters of regular trading vessels of less than 3,000 N.R.T. upon the Harbour Master’s satisfaction of their competence. ” (ADNOC - PPA, 2013)

Since those vessels are not influenced by any depth restriction they can choose to follow any of the routes (avoiding route “a” which is the busiest one). Therefore, the most logical decision is to take the shortest itinerary (route “e”) which leads directly to the inner anchorage area avoiding to pass through the also busy one-way sections which are later introduced.

All non-exempted vessels follow route “a” which does not impose depth or traffic limitations to any vessel or combination of vessels. Therefore, in practice only two of those routes are used: route “a”, which is taken by the majority of the vessels, and route “e”.

After sailing through route “a”, two options are available for entering the port (Figure 3-9):

f) S4 + S5 + S6 (Deep Water Channel) g) S4 + S5 + S7 (Stewart Channel)

Figure 3-9 Possible routes from outer anchorage to the inner anchorage.

a) c)b) d) e)S1S2

S3

S1S2

S9

S10

S1S2

S9

S11

S12

S1

S8

S10

S1

S8

S11

S12

g)f)S4

S5

S6

S5a

S4S5

S7

S5a

22


Section S4 does not impose any depth or traffic restriction. All other sections are one-way operated meaning that no overtaking or encounters are allowed. Additionally priority at these sections is given to outgoing vessels therefore incoming vessels may have to wait for the channel to be cleared. The choice for one of the routes is also dictated by the vessel´s draught. Only vessels with a draught smaller than 9m can sail safely through S7. No tidal windows apply for incoming vessels since the terminals of the Jebel Dhanna/Ruwais Port provide loading facilities and the vessels arrive lighter than they leave.

In summary, there are two kinds of vessels which sail from the outer to inner anchorage and vice-versa: the ones that can sail through any of the routes and the deep draught ones which can only sail through the deep water channel. The final route decision is made based on the combination of these two restrictions: the depth and the one-way operation with priority for outgoing vessels.

Due to the existence of an inner anchorage, vessels can always enter the port irrespective of the berth availability. When a berth is available at the terminal of destination, the vessel can sail directly to the berth. Otherwise she will have to wait at the inner anchorage for a berth to become available.

At the berth, the cargo handling services take place and after finishing loading the departure procedure begins.

In fact, due to Port Regulations, the verifications for outgoing vessels are conducted at least 2 hours before departure:

“The Master or Agent of any ship within the port shall inform the port control giving at least four hours in advance a notice of departure and shall further confirm the time of departure two hours before being ready to sail.” (ADNOC - PPA, 2013)

When the vessel demands permission to leave the berth, first thing to be checked is whether it is a pilot exempted vessel or not. The division of routes between exempted and non-exempted vessels is the same as for incoming vessels. In the case it is an exempted vessel, any of the routes can be followed and since in the current situation exempted vessel do not have any depth restrictions, route “e” is again selected for the same reason described for the incoming vessel. Hence, the outgoing exempted vessels leave the port through route “e”.

All other vessels follow route “a”. However, the selection between routes “f” and “g” has to be done and for outgoing vessels this verification is less straightforward than for incoming ones.

As already mentioned, incoming vessels do not have any tidal restrictions when sailing through S6. Conversely, some outgoing vessels are tidal bounded at S6 and S5a and additionally, fewer vessels can use S7 when leaving the port due to the 9m draught limit.

When the involved vessel is not tidal bounded, either route “f” or “g” is selected based on the vessel’s draught. The selected route is reserved for the outgoing vessel during the period at which the vessel is foreseen to be sailing through it. This reservation is required in order to ensure its priority while sailing through the one-way sections.

Nevertheless, if the vessel is subjected to a tidal window restriction, the tide prediction for the moment at which she would be sailing at the depth restricted sections has to be checked and the minimum required tide should be met during the entire journey. When the window is opened at the moment of request, the same procedure of a non-tidal bounded vessel applies: the selected route is reserved and the vessel can sail out of the port immediately after leaving the berth. Nevertheless, if the conditions are not directly met, the next possible open window should be identified and reserved. The vessel should then leave the berth and wait at the inner anchorage while the window is closed.

All the verifications involving water depth restrictions take into account the time that it takes for the vessel to get from the verification point to the depth restricted section and to sail through it.

23


Examples of real operations obtained at MarineTraffic (2014) are presented in Appendix A for illustration purposes. The next paragraph schematizes the just explained operations and defines the processes that are going to be modeled.

Processes definition 3.6

In order to translate the above described navigational service system into a model, a schematization is required. Simplifications are needed and assumptions have to be made. The following flowchart (Figure 3-10) presents the schematization of processes and verifications specific to the Port of Jebel Dhanna/Ruwais. This flowchart is an extension of the generalized flowchart presented in Figure 3-5.

Figure 3-10 Flowchart of The Port of Jebel Dhanna/Ruwais processes.

Inco

min

g Ve

ssel

sO

utgo

ing

vess

els

NoNo

Delays

Traffic control

Depth restrictions

Terminal Master

Quay operations

Pilotage exemption certificate?

VESSEL ARRIVES

Sail through route “a”

Terminal master:Berth available?

Sail to berth through route

“e”

Sail to inner anchorage

through route “e”

Wait at inner anchorage until

berth is available

Sail to berth

Traffic control:Encounters at route

“g”?

Traffic control:Encounters at route

“f”?

Sail to inner anchorage

through route “f” or “g”

Terminal master:Berth available?

Wait at inner anchorage until

berth is available

Sail to berth through route

“f” or “g”

Sail to berth

Pilotage exemption certificate?

When ready to leave: Sail out

of Port through route “e”

Look for next window to sail

through S6+S5a

Book S6 + S5 (route “f”)

Book S7 + S5(route “g”)

Can the vessel leave the port directly after

leaving berth?

Sail through booked route

Wait at inner anchorage until window is open

Leave Port through route

“a”

Quay operations

Ask for clearance 2

hours prior to departure

Draught < 9mTidal bounded vessel?

Wait at outer anchorage unitl

route is free

Yes

No

Yes

No

Yes

Yes

Yes

No

Yes

No

YesNo

No

Yes

Book S6 + S5 (route “f”)

Yes

No

Draught < 9m

Yes

No

24


The processes of vessels arrival, verifications for clearance, route assignment, vessels sailing and quay operations are detailed below.

Vessels arrival 3.6.1

The arrival process of vessels in a port is stochastic in character. The interval between successive vessel arrivals can be represented by a statistical distribution. When arrivals are completely random, the most appropriate inter-arrival time distribution to be used is the Negative Exponential Distribution (NED) (Groenveld, 2001). However, when a tendency towards some scheduling of arrivals is noticed, a more general distribution such as the Erlang-k distribution applies. Both distributions are related to each other in such a way that Erlang-1 is equal to NED.

Kuo, Huang, Wu, and Cheng (2006) have studied the inter arrival distribution of container vessels and their findings show that the Erlang-k distribution applies. However, the k number happens to be a function of the observation system scale. When looking at a single berth used by a single shipping line, k was found to be equal to 5. As the system’s scale grows the k number tends to decrease reaching a value of 2 for a dedicated berth with more than one shipping lines and of 1 to a single terminal or for the entire port.

For the Port of Jebel/Dhanna Ruwais case study, the inter-arrival times are going to be analyzed from a terminal perspective. Therefore, even though some scheduling of arrivals occurs, the scale of the observed sub-systems (terminals of the port) implies on the use of an Erlang-1 distribution. For that reason, Erlang-1 inter-arrival distributions are established for every single terminal of the case study port.

Verifications for clearance and route assignment 3.6.2

The clearance verifications that have to be performed by the Harbour Master during the vessel passage through the port in order to ensure the practice of the Port Regulations are summarized in the following list:

Is the port operating? Does the weather conditions allows for the safe passage of vessels? Does the tide stage enable safe navigation? (Tidal window) Can the traffic rules be respected during the entire trajectory to be followed? (Traffic control) Is there a berth available for the vessel to be served? (Terminal Master)

The Port of Jebel Dhanna/Ruwais is operated 24 hours daily for all 365 days a year. Therefore, the first verification can be omitted. The others are now described.

Weather conditions

Regarding the weather conditions, the Port Authority has stated that there is no significant navigation downtime due to adverse weather conditions. Therefore, the port will be considered to be open all the time irrespective to the wave and wind conditions.

However, it is known that the Shamal or strong North West wind may blow for periods of three to five days and raise a rough sea with waves up to 4.5 meters (ADNOC - PPA, 2013). The 3-5 day Shamal typically occurs once or twice each winter, from December to February (Perrone, 1979).

During such an event, the navigational services of the port are stopped. No entrance of incoming vessels is allowed, and arriving vessels should wait outside. Vessels that are being served at the berths should finish their loading operation and wait at berth until the port is opened again. Vessels waiting at the anchorages keep waiting until the operations restart. As soon as it happens, loaded vessels can leave and incoming vessels resume their journey reaching their destination terminal.

25


Since no navigational service is provided to vessels while the Shamal is blowing, this situation is not reproduced by the model. In reality, it would take same time after the port reopening for the steady situation to be restored but this period is not considered to be representative for the overall navigational services performance.

Traffic control

The route to be followed by the vessel at the Port of Jebel Dhanna/Ruwais depends largely on the grant of pilotage exemption certificates. Only when having a Pilotage Exemption Certificate, the vessel can sail through other routes than the Main Access Channel (route “a”). The route is then defined based on this information and on the depth restrictions. Additionally, the traffic rules, such as one-way operation and vessel prioritization, have to be followed and may have impact on the route definition and on waiting times.

At the one-way operated sections, neither encounters nor overtaking manoeuvres are allowed. Vessels sailing in the same direction can follow each other in a convoy with the speed defined by the slowest vessel ahead. The traffic control may also ensure that outgoing vessels have priority and should be able to leave the port as soon as possible after being served at the terminals. Therefore, two hours prior to finishing the quay operations, the vessel has to inform the traffic control the time at which she will be ready for sailing and request for clearance. This two hours requirement is stated at the Port Regulations in order to ensure enough planning time to guarantee outgoing vessels priority. The traffic control has then to check for the pilotage exemption and the tidal window and reserve the timeslot at which the vessel is planned to be sailing through the one-way section. An example of the reservation procedure for three subsequent outgoing vessels is presented in Figure 3-11.

Figure 3-11 Reservation of one-way section for outgoing vessels.

All three vessels in the example do not have a pilotage exemption certificate and are not bound by a tidal window. Vessels 1 and 2 request for leaving the port through S6. Vessel 2 will enter S6 when vessel 1 is almost leaving it. Therefore, section S6 is reserved for outgoing vessels until the last one clears it. Vessel 3 leaves through S7. Section S5 is blocked by all the vessels. Blank spaces can be used by incoming vessels as long as the draught limitations and the encountering restrictions are met.

Request for departure

Entrance routes “f” and “g”

End of S5

S6S6 1 2

S7S7 3

S5S5

1 3Last 2 hours of quay operations

Unberthing

Section reserved for outgoing vessels

Sailing out of berth

Sailing through S6

Sailing through S7Sailing through S5

VESSEL 1

VESSEL 2

VESSEL 3

Time

2

26


It is important to highlight that the reservation of the one-way sections of the channel for outgoing vessels should be executed for all non-exempted vessels. Exempted vessels sail through the Relief Route and therefore do not affect the traffic in the one-way sections. Non-depth restricted vessels should be able to leave the port immediately and tidal-bounded vessels may have to wait for the high water reserved timeslot. The tidal window verification and the high water reservation are explained below.

Tidal window

Tides at the port location are diurnal. Tidal currents are negligible at the case study port therefore horizontal tidal windows do not apply. Sections S6 and S5a of the access channels (which are part of route “f”) are limited in depth for some laden/outgoing vessels during low water.

The verification of this vertical tidal window has to account for the vessel’s draught, the limit underkeel clearance (UKC), the water depth at Chart Datum (CD) and the tide. The definition of the minimum required tide is presented in Figure 3-12. Additionally, the length of the channel and the vessel speed should be considered to ensure that the window is opened during the complete passage.

Figure 3-12 Minimum required tide definition (not to scale).

There are three possible situations that can occur for the tidal bounded outgoing vessels.

1. The window is opened at the exact moment the vessel is planned to reach the entrance of the depth limited section. In that case the vessel sails directly out of the port.

2. The window is not immediately opened. Then, the vessel has to wait at the inner anchorage for high water.

3. The window will close during the period at which the vessel is planned to be sailing through the depth restricted section. In that case the vessel has to wait at the inner anchorage for the next high water.

When tidal bounded vessels are subject to delays, they are transferred to the inner anchorage leaving the berth free for the incoming vessels.

The above described occurrences are schematized in Figure 3-13. The horizontal axis represents the time and the vertical axis the water depth. When the tide reaches the minimum required level the water depth is suitable for sailing and the tidal window opens.

27


Figure 3-13 Tidal window verification and route reservation.

Terminal Master

The Harbour Master communicates with the Terminal Master in order to check for the berth availability and assign the berth of call to the incoming vessel. In this case study berths are assigned to vessels; however, in other cases, assigning a certain quay length instead of a berth is more appropriate. When all berths of the destination terminal are occupied, the vessel has to wait at the inner anchorage until authorization is given to sail to a just cleared berth.

Vessels sailing 3.6.3

The sailing process is defined as a discrete process; the length of the channels and the vessel speed is used for obtaining the time that it takes for the vessel to cross each section. The length of the channels can be directly obtained from the Admiralty Charts. The speed of the vessels is usually the maximum possible speed of the vessel which depends on parameters such as the vessel characteristics and the channel depth. Typical values are: 10 to 12 knots for bulk vessels and tankers and 15 knots for container and RoRo vessels.

Tidal window


S6 Entrance End of S5a

Tidal window



CD + Minimum required tide


Tidal window




Tidal window

Last 2 hours of quay operations

Sailing out of berth Unberthing

Sailing through S6

Waiting at inner anchorageRoute reserved for outgoing vessels

Sailing through S5a

CASE 1 – TIDAL WINDOW IS OPEN WHEN THE VESSEL GETS TO THE S6 ENTRACE

CASE 2 - TIDAL WINDOW IS CLOSED WHEN THE VESSEL GETS TO THE S6 ENTRACE

CASE 3 - TIDAL WINDOW WILL CLOSE WHILE THE VESSEL IS SAILING THROUGH S6

Time

Wat

er D

epth

28


Some characteristics of the channel can trigger a reduction on the vessel speed. For instance, it is known from previous studies that deep draught vessels pass over the shallows at the Deep Water Channel with a reduced speed of 8 knots in order to minimize squat. Some sharp bends may also decelerate the vessels. Sailing speeds at manoeuvring areas are assumed to be 5 knots and at the berthing approach 3 knots. The vessel sails with the minimum between the maximum speed of the vessel and the maximum speed of the channel.

Additionally there is a factor, with respect to the traffic regulations, which influences the sailing speed of the vessel. If the channel is two-way, overtaking is allowed and if a slower vessel is sailing ahead of a faster vessel, the fastest can overtake the other one. However, in a one-way operated channel, the fastest vessel will have to reduce her speed in order to keep a safe distance from the vessel which is sailing ahead.

All sections except S5, S6 and S7 are two-way paths, which means that vessels can encounter and overtake each other. Both manoeuvres are simulated by one vessel passing on top of the other. In other words, for overtaking manoeuvres, vessels with higher speed pass over the slower vessels without any deceleration or acceleration and keeping the same trajectory. For encountering manoeuvres the same trajectory is kept for both vessels and no deceleration or acceleration occur. Detailed simulations of these manoeuvres are usually performed during ship manoeuvring simulations at design stages and are out of the scope of this graduation work. Additionally, in this case study, the two-way sections are fairways with no lateral restrictions with the same range of water depths around the central alignment allowing for vessels to overtake even when there is another vessel sailing in the opposite direction. Moreover, vessels do not necessarily have to change their speed or direction while executing the overtaking manoeuvre. Therefore, the overlapping of vessels without changes in speed is not an immoderate assumption.

For the non-passing paths (S5, S6 and S7), a safety distance is usually maintained between the vessels sailing in a convoy. Typical values are equal to 5 times the vessel length. For the given case study, the maximum vessel length is 346m, when considering a sailing speed of 10 knots the distance in time corresponding to 5 times the vessel’s length is equal to 6 minutes. However for dangerous cargoes, separation times of 20 to 30 minutes might be required (PIANC, 2014a). More information about the approach used by the model for the spacing functionality is given in the simulation model chapter.

When moving between sections with different speeds, the vessel will immediately change her speed without any acceleration or deceleration. This assumption is considered adequate given the length of the channels and the minor difference in total sailing times as a result of this simplification. It is expected that the additional time required for accelerating and decelerating corresponds to a small percentage of the total sailing time and an even smaller fraction of the turnaround time.

Regarding the pilot boarding, there are three possible situations: 1) the incoming vessel is sailing through the channel and reduces her speed so the pilot can board (which is also the case for disembarking), 2) the vessel is waiting at the outer anchorage and the pilot boards when the vessel is still stopped or accelerating 3) the vessel is leaving the port and the pilot embarks while the vessel is still at berth. Therefore, for incoming vessels at the outer anchorage or to outgoing vessels at the berth, no additional time related to pilot boarding should be added to the sailing time. That is, in this case the time that it takes for the pilot to board is included in the waiting time or in the quay operations procedure. However, for incoming vessels that do not stop at anchorage and therefore have to decelerate for the pilot to board/disembark and accelerate again, an additional time would have to be added to the sailing time. Another possibility is to include the whole decelerating and accelerating procedure related to the pilot boarding operation in the model. Neither of these approaches is used; therefore, the time that it takes for the pilot to board/disembark is not considered in this simulation model.

29


In view of having considered all changes in speed within the model negligible, disregarding the time for the pilot to board and disembark (which corresponds to one deceleration followed by one acceleration period) is justifiable, by definition.

The berthing and unberthing procedures are part of the transition between the navigational services to vessels and the cargo handling services. In this graduation work, those processes are included in the quay operations as explained in the next paragraph.

Quay operations 3.6.4

The navigational services provided to vessels from their arrival to the berthing operations and from unberthing to vessels departure are investigated and evaluated during this graduation work. In between the berthing and unberthing the terminal and cargo handling activities are performed. These activities at the berth are modeled at high level only and the approach is described in this paragraph.

The Port of Jebel Dhanna/Ruwais has in 2014 six terminals with 26 berths in total. From 2016 a new terminal will start operating in phases until reaching 18 berths in commission in 2030. Each of these berths has its own restrictions regarding vessel dimensions, type of cargo and so on. Nevertheless, for planning stages, assigning a group of berths with similar conditions as the destination of the vessel is thought to be enough. Therefore, vessels have as their destination a given terminal (or group of berths) and use whichever berth is available among the assigned ones.

Although the Port of Jebel Dhanna/Ruwais in 2030 will have only eight terminals, 13 destinations are defined. That is because the Ruwais Refinery Terminal – TAKREER and the ChemaWEyaat terminal have different groups of berths as destinations. This distinction is necessary due to the fact that there are groups of berths within the same terminal with considerable differences in terms of maximum vessel dimensions, productivity rates and handled products. For example, the TAKREER terminal consists of two groups of berths, consequently two destinations: the deep water berths and the coastal tankers berths.

The distribution of vessel classes per terminal is predefined based on the berth restrictions in vessels dimensions therefore it is unnecessary to verify these conditions again for every calling vessel. In other words, only vessels that fit at a given group of berths will have this group as their destination.

The quay operations are simply modeled as a forced time at berth. The total time (Eq. 3.1) accounts for the berthing, the service time and the unberthing procedure. An additional time for paperwork, hose (dis)connection, (un)lashing and opening/closing holds is included (Eq. 3.2). Uniform distributions are used when no detailed information is available. In a more detailed phase of the project the berthing and unberthing procedures could be modelled for instance as dependent of the vessels size.

Tb = b + st + ub + et in minutes With: Tb = Total time at berth b = Berthing st = Service time ub = Unberthing et = Extra time

(Eq. 3.1)

et = uniform(1.5,2.5) ∗ 60 in minutes (Eq. 3.2)

The berthing procedure includes the final berth approaching and the mooring with the total time required for that ranging from 30 to 60 minutes (Eq. 3.3). Unberthing accounts for tugs making fast, unmooring and tugs pulling out of the berth. Usual values for the time spent in this process range from 30 to 60.

30


b = ub = uniform(30,60) in minutes (Eq. 3.3)

The loading/unloading operations duration (service time) is a function of the terminal of call and the amount of cargo to be handled. The amount of cargo is assumed to be a percentage of the DWT and the loading/unloading rates are given per terminal. The service time (Eq. 3.4) is established with these variables.

st =pl × DWT

P

With: DWT = deadweight tonnage pl = percentage of the total load to be handled P = productivity

(Eq. 3.4)

Since the berth and unberthing procedures are modeled together with the quay operations but they are not part of the cargo handling services, only the service time and the extra time are considered for the calculation of berth utilization.

After having presented the processes included in the model, the functionalities of the model are described.

Model functionalities 3.7

The model has to give an as close as possible representation of the real system. Therefore, the functionalities to be included in the model are the ones that are required to replicate the regulations and respective key verifications as described in the previous section.

Vertical tidal window; One-, two-way channel operation (encountering and overtaking rules); Traffic rules such as vessel prioritization and separation between vessels in a convoy; Routing of vessels based on Port Regulations.

In the next section the input parameters are introduced together with a discussion on the required level of detail. By discussing the required level of detail together with the input presentation it is intended to provide generic information in order to assist the selection of input in other cases than this case study.

Level of detail of input parameters 3.8

The level of detail of the input parameters required to perform the simulations is not known beforehand and recognizing it, at least for this case study, is a target of this graduation work. Therefore, a first approximation of required level of detail of input is made and some parameters, for which identifying the level is not straightforward, are selected to be further analyzed. The order of the following list was established based on the sequence of processes presented on section 3.6.

Traffic forecast

The traffic forecast is given as the number of calling vessels per year, per terminal and per class. When applicable, seasonal variations should be considered. No seasonal detailing is required for this master thesis case study. Based on these data, the inter-arrival distributions can be inferred.

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In this case study, the number of calls per year in every terminal is available. The fleet mix is obtained based on Sea-web data, on Royal HaskoningDHV experience and on the Port Regulations information, which gives the limits in dimensions and weight of vessels at every berth. Therefore, the definition of the percentage of vessels of each class calling at every terminal is supported by berth criteria data and experience based assumptions.

Port operation

The working days and hours of the port should be considered. In some situations a probability of exceptional events such as strikes or channel blockage and also the most probable duration of these events should be considered. For ports working only in daylight hours at low latitudes, a fixed day duration can be used as an approximation. However in high latitudes the daylight duration should be calculated specially if there are also variations in throughputs with the seasons. The case study port is open 24 hours 365 days a year and exceptional events are not taken into account.

Weather conditions

It is known that there are no significant delays at the case study port due to waves and winds, except for the Shamal condition which is not considered at the simulations. Additionally, the probability of having visibility reduction at the case study location is negligible and not taken into account. However, a brief discussion on weather conditions as an input for logistic models at planning stages is provided.

Waves: For planning stages, considering the probability of the waves to exceed the pilot boarding limit (Hs>1.5m) should be enough. However, if the wave climate and the traffic are seasonal, the seasonal wave statistics should be considered. The persistence of storms and calms can also be an important factor since storms that last longer may cause higher queues and the time that it takes for the system to restore could be significant.

Winds: Waves and winds are usually strongly correlated and downtimes due to wind conditions in many cases can be assumed to be at the same time as waves’ downtime. Additionally, wind is an important factor when designing the dimensions of the access channel, however, in not that extreme conditions, it should not significantly affect the traffic.

Visibility: The probability of having visibility reduction due to fog, dust, rain or a combination of them can also be considered.

Other weather issues such as ice formation should be included when applicable.

Tidal Window

Tides: In order to correctly predict the delays caused by the tidal window the tidal model requires a certain level of detail. In very simple cases, where only one vessel class is tidal bounded and also when there is only one critical water depth, a simple model indicating if the window is open or closed can be used. However, in a complex system, such as this case study, with many classes of tidal bounded vessels demanding different minimum tides for different channel sections, a more detailed representation of tide is required. Therefore, a predicted time series of water levels generated based on the amplitudes and phases of the harmonic constituents of the tide at the port are included in the model. The tidal constituents were obtained from the TPXO global model (Egbert, Bennett, & Foreman, 1994) and the time series was generated using the T_Tide toolbox (Pawlowicz, Beardsley, & Lentz, 2002). The tide is mainly diurnal meaning one High Water per day.

Tidal windows should be considered in such a way that the water depth is ensured during the entire vessel excursion. Therefore, it has to account for the vessel current draught (depending on the loading situation), the underkeel clearance and her sailing speed and track. This approach is used at the case study.

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Currents: The effect of currents on navigation can be considered, when needed, in the same way as the tide: by applying a horizontal tidal window. However, for the concerned port, tidal streams attain a maximum of 1 knot at the outer approaches and seldom exceed 0.4 knots at the berths area and therefore their effect is not included in the model.

Vessels’ characteristics

The draught of the vessel has to be known for every loading condition in order to route the vessels to the appropriate route and to verify the tidal window. For this case study every vessel class has a deterministic in ballast draught when arriving at the port and a deterministic laden draught when leaving the port. However, in some cases it might be required to introduce stochasticity to this characteristic or at least to represent it in more detail.

The speed is also required since the window has to be checked for the entire vessel excursion. The speed also determines the time spent by the vessel navigating through the channels. At planning stages the vessel maximum speed can be estimated and taken as deterministic. Acceleration and deceleration rates are not included in the model since the loss of accuracy when making this assumption in such a long network of access channels is negligible.

In some cases, additional information such as the length and the beam of the vessel are required for implementing traffic rules for example when the access channel are mixed operated and when the spacing of vessels in a convoy is defined based on the vessel’s length.

Port layout

Channel sections: The length and the minimum depth of the access channels have to be known. The length determines the sailing distances. The depth is needed when tidal windows are operated and also for assessing vessel’s maximum speed and others. Since the traffic rules for the case study are already determined, the width of the channels is not a required input. However, when other functionalities such as mixed operation of channels is to be tested the width of each channel section may be required, either for using it directly to perform verifications of encountering and overtaking manoeuvres or to derive rules which establish for each combination of vessel classes the possibility or not to perform such manoeuvres at different channel sections. In any of the cases, another input addition is required: the beam of the vessels.

Terminals: The number of berths determines the amount of vessels that can be served at the same time. The service rates (loading and unloading) and the amount of cargo to be handled determine the time spent by each vessel at berth. For the case study, the berths are grouped in ranges of services rates within each terminal. Initially the time at berth is considered to be deterministic; however, a sensitivity analysis is performed to analyze the influence of this boundary on the model results by assigning a distribution for it.

Anchorages: The location of the anchorages is important since it determines the entrances and exits from the access channels. At planning stages, the anchorage can be considered unlimited and keeping track of the number of vessels using it should be enough in order to assess the required anchorage capacity. This approach is used for the case study. However, when intermediate anchorages have very limited number of places, it might influence the delays in the port and the availability of those anchorages should be considered in more detail. For instance, if the inner anchorage of the case study port becomes too congested the number of places should be limited and the effect of it on the model results should be evaluated. This limitation is not considered from the very beginning for simplicity reasons.

Traffic rules

The case study Port Regulations presents all information about traffic rules currently applied at the port. Therefore, for the first stage of this graduation work, when the current situation is simulated, those traffic rules are imposed to the vessels.

33


However, for the planning stages of a new port development, the rules may be not yet established. In this case, a simplification of the overtaking and encounter manoeuvres regulations can be done by defining for every vessel class combination the possibility or not to execute each manoeuvre at every different section of the channel. When a mixed-operation is applied, the combinations of vessel which can use the channel as a two-way channel have to be specified. Another way of determining the traffic rules is by simply imposing a one-, two-way or mixed channel and performing the verification based on vessel dimensions and safety limits.

The separation between vessels in a convoy can be simplified by a fixed value (of time or space). At the simulation model, the safety distance regulation will be included. A sensitivity check will be performed by eliminating the spacing restrictions and comparing the results in order to verify if the increase in the level of detail significantly change the outputs.

Priority rules, when existing, should be considered at the models in order to better represent the real situation. At the case study, all outgoing vessels have priority. This priority is ensured by predicting the time during which the outgoing vessel will be using the one-way sections and blocking the passage for incoming vessels. If more vessels are claiming for the leaving the port through the one-way sections in a sequence, the channel is blocked for the total duration of vessels sailing out and only when a sufficient long window is opened between two succeeding outward sailing vessels, incoming vessels can get into the port. Therefore, the arrangement of outgoing vessels determines the change in sailing direction of the one-way sections.

Additional input or a higher level of detail may be needed in case of adjustments in the Port Regulations, changes in operations, inclusion of new functionalities and modification of model requirements.

A list with the inputs for which a verification of the influence of the level of detail will be performed is given:

Safety distance: As a first estimate a 30 minutes spacing is considered in order to not overestimate the port capacity. At the sensitivity analysis, the influence of reducing the spacing to 15min or to no spacing at all is tested.

Time spent at berth: this is the sum of berthing, service time, unberthing and extra time. Initially the service time is assumed deterministic. Afterwards, a distribution is assigned and the changes in model output verified.

Model output requirements 3.9

For this specific case study the model should be able to provide results in order to support the port planner in performing the following tasks:

1. To assess the ability of the port to handle the forecasted traffic and throughput; 2. To investigate the development of congestion with increasing traffic; 3. To assess the performance of the port with increasing traffic; 4. To identify bottlenecks (sources of delay); 5. Estimate the number of pilots and tugs needed to provide the services.

This list was already presented at section 3.1. However, it is repeated here in order to facilitate the presentation of output requirements.

After a brainstorming session with port planners, the Model Outputs and Key Performance Indicators (KPIs) needed to accomplish these tasks were identified. The model outputs and the KPIs which are required for performing every listed task are given below. Also, the model KPIs definition is presented as well as the model outputs needed for calculating them.

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Task number 1 - To assess the ability of the port to handle the forecasted traffic and throughput

This task can be accomplished by comparing the forecasted traffic and throughput with the actual number of vessels and cargo handled by the port in the simulation. Thus, the model outputs needed for performing this task are: number of served vessels (per vessel class) at the different terminals and respective throughput.

Task number 2 - To investigate the development of congestion with increasing traffic

Task number two can be accomplished by keeping track of waiting times and the number of vessels that have to wait at the anchorages. Thus, the model outputs needed for performing this task are: waiting times and number vessels at the different anchorages.

Task number 3 - To assess the performance of the port with increasing traffic

This task requires performance indicators as a tool of measurement of port performance. For planning stages, essential KPIs were identified and are presented below:

I. Waiting time / turnaround time; II. Waiting time / service time (average rate of waiting);

III. Berth occupancy.

The KPIs are not directly calculated by the model but the model outputs are used for calculating them. The definition of the selected KPIs is now given in order to determine which model outputs are required for calculating them. The KPIs can be calculated per vessel class, per specific berth or terminal or for the whole port depending on the level of detail required. Usually the data is analyzed on a yearly basis. However, in some situations, monthly statistics should be more appropriate. Since the focus is given to marine operations, no performance indicators related to quay, handling or storage operations are required.

I. Waiting time / turnaround time

This performance indicator (Eq. 3.6) gives an idea of the magnitude of the delay (Eq. 3.5) compared to the total turnaround time. Evaluating the tolerability of a single value of waiting time by itself can be rather difficult. For instance, one hour of waiting time has a totally different meaning for vessels with a turnaround time of some hours or some days. Therefore, the perception if delays are acceptable or not is in this case evaluated with respect to the total time in port.

WT = �wt With: WT = total waiting time wt = waiting time at every anchorage

(Eq. 3.5)

KPI1 =

WTTR

∗ 100 With: TR = turnaround time KPI1 = rate of waiting w.r.t. the turnaround time (%)

(Eq. 3.6)

Since the marine operations are to be evaluated and both the waiting time and turnaround time include waiting for berth and service time which are both related to cargo handling services, the waiting time due to navigational services issues and the turnaround time excluding these shares are better indicators. The resulting formulae are (MO for marine operations):

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WTMO = WT − WTb (Eq. 3.7)

With: WTMO = waiting time due to marine operations WTb = waiting time due to berth unavailability

TRMO = TR – (st + et + WTb )

(Eq. 3.8)

KPI1𝑀𝑂 =WTMOTRMO

∗ 100 (Eq. 3.9)

With: TRMO = marine operations turnaround time KPI1𝑀𝑂= rate of waiting w.r.t. the turnaround time (%) marine operations only

When the formula is presented in this way, the turnaround time due to navigational services is actually the service time (including delays) of the marine operations in contrast to the service time presented in the next KPI which is related to the quay operations.

This rate is calculated for all vessels separately. Statistics can be obtained for the different terminals or for the entire port. Additional rates can be obtained for every different cause of delay (due to marine operations), for instance the rate of waiting due to tide, traffic or spacing w.r.t. the marine operations turnaround time.

From a perspective of the port, these are good performance indicators. However, for ship owners (the customers), a better perception of acceptability can be achieved by comparing the waiting time with the service time which from a customer perspective is the service time of the quay operations.

II. Waiting time / service time

This KPI (Eq. 3.10) is a measure of the port performance as a whole and is not directly related to the navigational services performance. However, it is usually applied to check ship owners requirements since for customers it is easy to evaluate a service based on the relation of waiting time with respect to the duration of the actual service.

KPI2 =WTst

∗ 100

(Eq. 3.10)

With: KPI2= rate of waiting w.r.t. the service time (%)

III. Berth occupancy

Berth occupancy (Eq. 3.12) is the ratio of time the berth is occupied by vessels being served (Eq. 3.11) to the total time available in that period. High berth occupancy (>70%) is a sign of congestion at the port and hence decline of services, while low berth occupancy (<50%) suggests underutilization of resources (Brooks, 2012).

ST = st + et (Eq. 3.11)

BO =∑ STTA

∗ 100

With: BO = berth occupancy (%) TA = total time available

(Eq. 3.12)

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From the Port Authority’s viewpoint, the higher the occupancy is, the less the costs are. From the ship owners’ perspective, the higher the occupancy is, the higher the costs are due to the higher waiting times. The optimum occupancy is the one that maximizes both sides’ net benefits (de Weille & Ray, 1974).

There are others indicators to be used as a sign of congestion however for this case study this one is selected in order to be possible to quickly assess if improvements at the navigational services will also improve the port performance. This KPI is of main importance for having an overall idea of the port utilization. For instance, if the berth occupancy is relatively high but the waiting times are mainly due to the one-way operation of the access channel, improvements on the channel may only shift the main cause of delays from the channel bottleneck to the berth unavailability. Therefore, only when the berth occupancy can still be increased by improving the utilization of resources, enhancing the navigational services performance will have a positive effect on the overall port performance.

Task number 4 - To identify bottlenecks (sources of delay)

Task number four requires that not only the delays must be quantified but also the reason for the delay. The sources of delay considered for this case study are:

Tidal window; Berth unavailability; Channel unavailability due to traffic regulations (Spacing and Outgoing Traffic in one-way

sections).

Waiting times due to other causes such as maintenance or breakdown are not considered.

Task number 5- Estimate the number of pilots and tugs needed to provide the services

Task number five can only be executed in a simplified way at port planning stages. Keeping track of all pilots and tugs activities require a high level of detail which is contradictory to the time and information available at these early stages. However, since having an estimate of these figures is a common request of clients even at this stage, the assessment can be simplified.

Regarding the tugs, the simplification is done as follows: all vessels are assisted by 2 tugs. The tugs make fast at different locations in the access channels depending on the vessels destination. They guide the vessels to the berth. After berthing they are released and come back to the vessel at the moment of departure. For vessels calling at the ADCO terminal one of the two tugs is not released and stays connected to the vessels during the loading procedures at the SPMs. Transfer time from one vessel to the next one is not considered, therefore immediately after serving one vessel, the tug is considered to be free to assist another one.

Pilots board the non-exempted vessels at the Ghasha Pilot Station and leave the vessel when it gets to the berth. In the way out of the port, he embarks while at the berth and disembarks at the Ghasha Pilot Station. Besides de simplification of not considering the transfer times as it is done also for the tugs another simplification apply to the pilots. During waiting times, either due to berth unavailability for incoming vessels, or for the tidal window and spacing of outgoing vessels, the pilot does not disembark. In reality when waiting times are expected to be high the pilot might disembark in some occasions.

Therefore, for tugs, it is expected that the model can underestimate the number of tugs operating at the same time since they are considered available immediately after assisting one vessel. Regarding the pilots, the model can underestimate the number of pilots operating at the same time for the same reason as for tugs and can overestimate it since pilots do not disembark from vessel at the inner anchorage with expected high waiting time. Additionally, in order to obtain the real number of required pilots, the working shifts have to be taken into account which is not done in this graduation work.

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Based on the above description of tasks to be performed supported by the simulation model results, a list of required model outputs is obtained:

a. Waiting time at anchorages; b. Cause of the waiting time; c. Number of waiting vessels at anchorages; d. Service time; e. Turnaround time (time from vessel arrival to vessel departure); f. Number of served vessels; g. Number of pilots on board; h. Number of operating tugs; i. Throughput.

The relation between the tasks, the KPIs and the model outputs is presented at Table 3-1.

Table 3-1 Relation between tasks supported by model results, model outputs and KPIs.

MODEL OUTPUTS KPI I KPI II KPI III Task 1 f and i Task 2 a and c Task 3 a, e and f a, d and f d and f Task 4 b x Task 5 g and h

The above mentioned model outputs and KPIs are assumed to be enough for evaluating existing ports and new port developments at port planning stages. However, this list may vary with the client demand and with requirements for specific cases. The soundness of this assumption is discussed later on this report.

Model assumptions 3.10

This section presents the assumptions taken for modelling the real system. This list consists of a compilation of all assumptions discussed throughout section 3, no new information is added.

Traffic forecast:

The fleet mix was defined based on the berth criteria given in the Port Regulations, Sea-web data and experience.

Verification for clearance:

The port is always open, irrespective of weather conditions.

Vessel kinematics:

The speed of the vessel does not change during encountering and overtaking manoeuvres. The vessel does not accelerate or decelerates and also the track is not changed. In other words vessels pass on top of each other in both manoeuvres.

No acceleration or deceleration rates are taken into account; vessels change their speed instantaneously when required. The influence of this assumption in the model results is to be checked.

One-way channels require a minimum spacing between vessels sailing in a convoy. This distance (in time) is considered to be the same for all combinations of vessels.

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Anchorages:

The anchorages are assumed to have unlimited amount of places available. Nevertheless, the number of vessels at each anchorage will be monitored

The movement of the vessel to and from the anchorage is not modelled. The time that it takes to sail to and from the anchorage is considered to be an additional time to the non-delayed vessel sailing time, and therefore is accounted as a waiting time.

Terminals and quay operations:

Maintenance, breakdown and other downtimes of terminals are not considered. The destination of the vessels is assigned to a terminal rather than to a berth. Berths of the

same terminal are gathered into groups with the same service rates (loading and unloading productivities) and vessels size restrictions.

The service rates of berths which load different products with different rates are assumed to be the average of all products loading productivities.

The amount of cargo to be handled is assumed as the vessel’s DWT times a percentage of cargo to be handled.

Pilotage and tug operations:

Pilotage is compulsory for all vessels entering the port except the exempted ones. There will be no limitations in the number of pilots available.

The number of pilots on board is monitored however no transfer time between operations is considered. Pilots stay on board of vessels at the inner anchorage even when high waiting times are expected.

The time that it takes for the pilot to board or disembark is not considered. The tugs operation is included indirectly in the model by assigning the tug-assisted speed to

the sections of the channels at which the vessels are assisted by tugs. The time that it takes for tugs to make/release fast is considered to be included in the sailing with tug-assisted speed process.

There will be no limitations on the number of tugs. The number of tugs in operation is monitored however no transfer time between operations

is accounted for.

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4 SIMULATION MODEL IMPLEMENTATION

This chapter presents the translation of the verbal model described in Chapter 3 to the simulation model. In paragraph 4.1 an introduction to FlexSim is given. The objects used in the simulation model are presented in order to acquaint the reader with the vocabulary which is used later in the model implementation description. Dedicated to the most enthusiastic readers, Appendix B presents more detailed FlexSim concepts which are interesting however not essential to the understanding of this chapter. Paragraph 4.2 explains how the processes presented in paragraph 3.6 are implemented in FlexSim.

Introduction to FlexSim 4.1

FlexSim is the simulation software used for translating the verbal model into a simulation model. The purpose of this paragraph is to present the objects used in the model implementation and therefore facilitate the interpretation of the next paragraph (4.2). All lists and examples are non-exhaustive.

FlexSim Objects

The FlexSim object groups used in this graduation work model are flowitems, fixed resources, travel networks and visual tools. Flowitems are the objects that move through the simulated process. Fixed resources are objects that handle flowitems in a certain way. The travel networks are composed by network nodes that give the paths to be followed by moving objects.

A brief description of those objects and how they are used in the case study model is now given. It is important to highlight that the FlexSim object library was originally created to simulate industry processes and abstractions have to be made while implementing a navigational service model.

Flowitems

Flowitems are the objects that are created and move through the simulated process. Vessels are simulated as TaskExecuterFlowItems. Differently from other flowitem types, this kind of flowitem can move through the model without the use of a task executer (transporter, crane, operator…) which corresponds exactly to the vessel within the port since they move by themselves. The vessel arrives (is created), then she follows a chain of processes (presented in the flow chart of Figure 3-10) and then leaves the port (is deleted). TaskSequences can be used to assign the order of destinations to be reached by the TaskExecutersFlowItems within the model. The choice of the TaskSequence to be followed can be based on the vessels’ characteristics such as the vessel class.

For every Vessel Class a FlowItem class is used. Every FlowItem class consists of vessels with the same properties such as type, size, maximum speed and initial labels.

Fixed resources

Source

The source is used to create the flowitems that travel through a model. Sources can create flowitems per an inter-arrival rate, per a scheduled arrival list, or simply from a defined arrival sequence. The vessel generators are source objects which simulate the vessels arrival.

Queue

The queue is used to store flowitems when a downstream object cannot directly accept a flowitem. By default, the queue works in a first-in-first-out manner, meaning that when the downstream object becomes available, the flowitem that has been waiting for that object the longest will leave the queue first. Other queue disciplines can be modelled, however only by manually programming it. Anchorages are queues in the navigational service system. In this model, as many

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anchorages as the number of causes of delay are used in order to identify the source of the delay and quantify waiting times separately. The just described default use of a queue can be used to model the Anchorage due to berth unavailability, where vessels have to wait until a berth at their destination becomes available. The traffic, spacing and tidal window “anchorages” require customization of the queue properties. For instance, tidal bounded vessels which are waiting at the anchorages have to be released as soon as the tidal window opens. This and other customizations are explained during the model implementation.

Queues, as well as other objects can be used whenever appropriate to route or perform modifications to flowitems. When the queues have the purpose of only assisting the conversion of the real system into a model but not of representing a storing physical part of the real system, they are referred to as “dummy” queues.

Processor

The processor is used to simulate the processing of flowitems. The process is simply modeled as a forced time delay. Processors are used to simulate the quay operations. The total process time is equal to the sum of the durations of berthing, unberthing, loading operations and an extra time for paperwork.

Sink

The sink is used to delete flowitems that are finished in the model. Once a flowitem has traveled into a sink, it cannot be recovered. A sink is used to eliminate outgoing vessels that leave the outer channel.

Travel Networks

NetworkNode

NetworkNodes are used to define the paths to be followed by the vessels. Some of the properties that can be assigned to a NetworkNode are connection type (no connection, passing, non-passing), spacing (minimum distance between vessels) and speed limit. “Passing” means that vessels do not back up along the path, but simply run over each other if speeds vary. Avoiding encounters or passing vessels when another vessel is coming in the other direction can only be done by programming new functionalities. “Non-passing” means that travelers along this path will actually back up, using the spacing value as a buffer distance between them. Therefore, the passing and non-passing connection types regulate only the overtaking manoeuvres. “Spacing” only applies if the path is Non-passing. This is the distance to be kept between the back of one vessel and the front of another vessel on the path. “Speed Limit” is a speed limit defined for the path. Vessels travel with the minimum of their own speed and the speed limit of the path.

Nevertheless, the default settings of the network connections are not sufficient to represent the traffic regulations at the Port of Jebel Dhanna Ruwais. For instance, passing and non-passing paths do not regulate encounters, only overtaking. Additionally, the default spacing regulations implies that vessels that do not meet the spacing requirements when getting to a NetworkNode, would wait at the NetworkNode until the minimum spacing is reached between her and the vessel which is sailing ahead. Once the condition is met, the vessel would enter the path and start sailing. This would not be a proper representation of reality therefore additional programing is required to introduce this functionality. Moreover, ensuring the priority for outgoing vessels is another regulation which cannot be implemented by using default utilities since a prediction of the time that the outgoing vessels are expected to be using the channel is required so that outgoing vessels are never waiting for the incoming ones to clear the one-way sections. The implementation of all these functionalities is explained in the following paragraph.

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Model implementation 4.2

Having now all FlexSim objects in mind, this section covers the implementation of the simulation model. The processes presented in the verbal model chapter have now to be translated into the simulation model. Information on how the implementation was done and the relation between the verbal and the simulation model are given. All the personalization required to simulate the Port of Jebel Dhanna/Ruwais navigational service system are detailed. Inputs to the model are presented whenever appropriate.

Vessels arrival 4.2.1

At the end of 2030, the Port of Jebel Dhanna / Ruwais will comprise 8 terminals in total. Among these 8 terminals, 13 different destinations were identified (Table 4-1). One destination is a group of berths within the same terminal that share the same productivity rates and vessel size restrictions.

Table 4-1 Jebel Dhanna / Ruwais Port destinations.

TERMINAL DESTINATION

1 01 Jebel Dhanna Terminal – ADCO (Crude oil)

2 02 Ruwais Refinery Terminal – TAKREER (Deep water) 03 Ruwais Refinery Terminal – TAKREER (Coastal tankers)

3 04 GASCO – Ruwais (LPG and Paraffinic Naphtha) 4 05 FERTIL – Ruwais (Urea, Ammonia, Ethylene) 5 06 GASCO – Ruwais (Sulphur) 6 07 BOROUGE (Polyethylene) 7 08 GASCO – Ruwais (Sulphur) 2

8

09 ChemaWEyaat - Liquids and gas (excl LNG) 10 ChemaWEyaat - LNG and FRSU 11 ChemaWEyaat - Bulk 12 ChemaWEyaat - Container 13 ChemaWEyaat - Construction

Since the number of vessels calling at every destination in one year is known, Erlang-1 (see section 3.6.1) inter-arrival distributions are derived for each of the 13 destinations. Thirteen sources (vessel generators), each corresponding to one destination, are then used to simulate the arrival of vessels to the port. A table containing the inter-arrival times for all simulated years corresponding to every destination is model input (Appendix C ). The distribution of vessel classes calling at every destination every simulated year is also model input (Table C-2). The fleet composition and the FlowItem Class corresponding to each vessel class are presented in Table 4-2.

Table 4-2 Fleet composition.

TYPE SIZE FLOWITEM CLASS

Bulk Vessel

3,000 DWT BV01 5,000 DWT BV02

15,000 DWT BV03 20,000 DWT BV04 40,000 DWT BV05 50,000 DWT BV06 60,000 DWT BV07

Tanker 120,000 DWT TK01 200,000 DWT TK02

Container Feeder 1,100 TEU CF01 1,200 TEU CF02

Container Vessel 5,000 TEU CV01 RoRo Vessel 9,000 DWT RR01 LNG Carrier 90,000 DWT LN01

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Based on the inter-arrival time distribution and the percentage of vessels of each class calling at each destination, vessels are generated and “arrive” at the port.

After the vessels are generated, they are assigned a number of characteristics. Some of the characteristics are specific for every vessel and others for vessels of the same class.

This information is stored at the vessel, either in its properties (which are limited and have their names and function predefined) or as labels attached to it, and can be assessed any time starting from its arrival. The labels corresponding to initial information (which are already assigned to the vessel class even before the vessel generation) are presented in Table 4-3. A brief description is given now and further explanation follows when describing the processes that redefine/use each label. Table C-4 shows the initial values for all vessel classes.

Table 4-3 Description of the initial vessel labels.

LABEL NAME TYPE DESCRIPTION 1 ID Fixed Indicates the order of arrival (specific for every vessel) 2 PilotageExemptionCertificate Fixed 1 for exempted vessels 0 for non-exempted (specific for every class) 3 Destination Fixed Established at the vessel arrival 4 Total_Cargo Fixed DWT or TEU (specific for every class) 5 Percentage_Cargo Fixed percentage of the total cargo to be handled 6 Outgoing Changeable 0 for incoming vessels 1 for outgoing vessels (changes at the berth)

7 DW_in Fixed 0 The incoming vessel can use either Stewart or Deep Water Channels 1 The incoming vessel can sail through the Deep Water Channel only

8 DW_out Fixed 0 The outgoing vessel can use either Stewart or Deep Water Channels 1 The outgoing vessel can sail through the Deep Water Channel only

9 Tidal_bounded Fixed 1 for tidal bounded vessels

The ID value is incremented every time a vessel arrives at the port and is accumulated over the entire simulation period. Label number 2 is used to route the vessels through either the Main Access Channel or the Relief Route as explained in section 3. Additional labels necessary for the proper execution of certain functionalities are added during the simulation and will be presented whenever appropriate.

The processes related to the clearance decisions when entering and leaving the port and the routing of vessels are now described.

Verifications for clearance and route assignment 4.2.2

A flowchart presenting the objects involved in the verification processes, and the choices to be taken are given in Figure 4-1.

In order to be able to recognize the causes of the waiting times, different queues are used to represent the same existing anchorage; therefore Anchorages are distinguished by their location at the model (Outer or Inner Anchorage) and by the cause of delay that has led the vessel into it.

Once the vessels leave their sources, they enter the first of the dummy queues. The OutsidePort dummy queue defines that exempted vessels follow route “e” to the inner anchorage and non-exempted vessels follow route “a” to the outer anchorage. In reality this decision is performed by the Harbour Master. Conversely, in the simulation model, this choice is performed locally since the decision is not affected by any other factor besides the possession of a pilotage exemption certificate.

All other verifications, which are dynamic and depend on the position and predicted track of other vessels, are centrally managed by the Harbour Master.

44


Figure 4-1 Flowchart of clearance decisions performed by the simulation model.

Exempted Vessel?

Dummy OutsidePort

Queue In

Dummy Outer AnchorageQueue 01

Dummy Inner AnchorageQueue 02

Encounters?

Berth Available?

Wait at Inner Anchorage

until Berth is available

Berth

No (a)

Yes (e)Wait at the Traffic Outer Anchorage until traffic clearance is

given

Enough spacing?

Wait at the Spacing Outer

Anchorage until spacing

time is reached

Yes

No

Yes

No

Request for leaving the Port (2hrs

prior to leaving berth)

Exempted Vessel?

Dummy Inner Anchorage

Out-Queue 01

No (a)

OutsidePort


Which Route?

Dummy Inner AnchorageQueue 01

Yes

S6

S7

Enough tide?

Wait at the Tide Inner Anchorage

until minimum required tide

is reached

Enough spacing?

Wait at the Spacing Inner

Anchorage until spacing

time is reached


Out-Queue 03

Which Route?

YesS7

S6

No

Yes (e)

Regu

late

d by

the

Harb

our M

aste

r

Regu

late

d lo

cally

Anch

orag

es re

late

d to

the

sour

ce

of th

e w

aitin

g tim

e

Vessel arrives



Out-Queue 02

Yes

No

No

Deci

sion

perf

orm

ed b

y th

e co

nnec

ted

obje

ct

Dummy InnerAnchorage

Out-Queue 04

45


The next step for exempted vessels is to be allocated to their destination. In case all berths at the destination are occupied the vessel waits at the inner anchorage until clearance is given. Non-exempted vessels have to pass through other verifications before proceeding to the berth. A set of dummy queues and Anchorages communicate with the Harbour Master which assists the verifications concerning the outgoing traffic on the one-way sections and the spacing when vessels are sailing in convoy.

In order to do so, once the Harbour Master is contacted by the incoming vessel it uses the vessel’s characteristics (more precisely, her speed) and the channel distances to predict the times that the vessel would be sailing through the one-way sections. The same is done when outgoing vessels request for leaving the port. With both timetables (incoming and outgoing vessels predicted times) filled up by the Harbour Master, the conditions for safe navigation can be verified.

The one-way channels are once again used to explain the modelling principles. Figure 4-2 (left) presents the scheme of channels between the outer and the inner anchorages. S5, S6 and S7 are the one-way sections. S4 is a two-way section which connects the outer anchorage to S5. S7 has a maximum allowed draught of 9 meters. S6 and S5a are depth restricted for tidal bounded vessels. S5 and S4 do not impose depth restrictions to any vessel class. Figure 4-2 (right) shows the traffic density map of the location with reddish colours corresponding to the most utilized routes.

Figure 4-2 One-way sections and corresponding traffic density map (MarineTraffic, 2014).

Now the decisions for clearance of outgoing vessels are discussed followed by the incoming vessels. This inverse order is required since the verifications for incoming vessels depend on the outgoing vessels, which have priority.

Outgoing Vessels

Two hours prior to leaving the berth, the vessel contacts the Harbour Master. If the vessel requesting clearance to leave the port is an exempted one, no other permissions are needed and as soon as the vessel is ready to leave, she will exit the port via the Relief Route. Otherwise, the Harbour Master has to fill out the outgoing predicted timetable for the applying vessel. While doing so, the Harbour Master has to ensure both the minimum required tide and spacing.

Spacing regulation

The spacing clearance concerns a safety distance to be kept between vessels sailing in the same direction. If a vessel is predicted to arrive at the entrance of the one-way channels with an interval smaller than the minimum spacing, the vessel is “sent” to an anchorage where she stays until the required spacing is met. Then, the vessel sails through the one-way section with the speed of the

GHASHA BUOY

OUTERANCHORAGE

INNERANCHORAGE

S4 S5

S6

S7

S5a

46


slowest vessel ahead. When exiting the one-way sections, the difference between the actual sailed time and the time it would have taken if no vessel was sailing ahead (no speed reduction) is added to the waiting time due to the spacing regulation.

The spacing control is performed as follows. By verifying the outgoing vessels timetable, the Harbour Master has to ensure that, irrespective of the followed route, vessels will keep the minimum spacing until the end of the one-way section. First, the time that the vessel is estimated to arrive at the entrance of S6/S7 is calculated. If the interval between subsequent vessels is not large enough, the predicted times for the trailing vessel are recalculated. Since there are two possible routes with different lengths, entering the channels with the required interval is not enough to ensure that they will be apart by the same distance at the end of the channel. Therefore, vessels should arrive at S5a with the same spacing interval. This condition is verified and when required, the estimated times are recalculated.

Since the distance from the various berths to the entrance of S6/S7 vary significantly, and also considering that vessels have different speeds there is always the possibility of vessels to get to the entrance of S6/S7 before the ones that are already in the timetable. The priority order is given by checking if the vessel which arrives at the entrance of the one-way channels first, arrives sufficiently in advance (if the spacing with the following vessel will be respected). If that is the case the vessel which has later requested to leave than the one which is already in the list can sail out of port immediately. However, if the interval between the successive vessels is less than the spacing, the vessel which requested clearance first will leave first and even though the other vessel gets earlier to S6/S7 she will have to wait.

This situation occurs quite often when tidal bounded vessels are at the timetable. While they are waiting for the tidal window other vessels which request clearance afterwards can leave as long as they do not cause any delay for the already waiting vessels.

After performing all checks, the time that the vessel is allowed to enter the one-way section is stored at the vessel and at the moment the vessel arrives at the channel entrance, if it is not yet the right time for her to leave, she waits at the Spacing Inner Anchorage until clearance is given. This time spent at the anchorage is equal to the difference between the initially expected arrival at S6/S7 and the actual entry at S6/S7. As already mentioned, the total delay due to the spacing regulation is equal to the time spent at the anchorage and the extra sailing time due to the speed reduction.

Tidal window verification

The tidal window was implemented exactly as described in the verbal model chapter (see Figure 3-13). The resolution of the tide in the model is ten minutes. The tidal window should be open between the entry time in S7 and the exit time of S5a. Vessels of class CV01 require 1m of tide and vessels of classes TK01 and TK02 require 1.3m. In case the tidal level does not allow the vessel to leave the port immediately after finishing the quay operations, the Harbour Master predicts when the next window will occur and inform the outgoing vessel. The vessel is then directed to the Tide Inner Anchorage and waits until the authorized time. If two tidal bounded vessels of the same class are waiting for the minimum required tide, they cannot leave the anchorage at the same time (once the tidal window is opened) because of the spacing regulation. Therefore, the Harbour Master has to include the spacing time between vessels at the predicted timetable and has to recheck if the tidal window will still be open during the entire delayed vessel’s journey. If the vessel is forced to wait for the next tidal window because of the spacing regulation, the extra waiting time, in addition to the waiting time for the first tidal window, is attributed to the spacing regulation and not to the tidal window.

47


Incoming Vessels

Now that the outgoing vessel procedure is described, the incoming vessel regulations can be explained. Since vessels enter the port in ballast conditions, the only hindrances to incoming vessels is the traffic flow of outgoing vessels in the one-way sections and the spacing between incoming vessels.

Traffic regulation

The traffic verification consists of ensuring that the incoming vessel will not encounter any outgoing vessel (which has priority) while sailing through the one-way sections. Therefore, by comparing the incoming and outgoing vessel timetable, it is possible to identify if vessels in both directions are estimated to be using the one-way sections at the same time. If that is the case, the incoming vessel has to wait at the outer anchorage until the channel is cleared. Since the scheduling of outgoing vessels is a dynamic process, the verification for traffic has to be performed multiple times. First time that the verification occurs is when the incoming vessel gets to the Ghasha buoy. If the vessel has to wait due to the traffic, once the outgoing vessel exists the one-way sections (end of S5 – way out) a message is sent to the Harbour Master in order to recalculate the incoming timetable for all vessels present at the Traffic Outer Anchorage.

If more than one vessel is at the Anchorage, the verification for encounters has to take into account also the delay due to spacing restrictions. Therefore, both verifications are combined in such a way that the clearance is checked iteratively and the waiting times are computed separately for each cause.

Since there are incoming vessels which can follow either route “f” (Deep Water Channel) or “g” (Stewart Channel), once the verification for both routes is performed and clearance is given, the selected route is designated.

Spacing verification

For the spacing verification of arriving vessels only the timetable for incoming vessels is required to be checked. The same procedure as explained for outgoing vessels applies. However, now the points of verification are the entrance of S5 and the exit of S6/S7. The spacing verification is always taking into account the route to be sailed which is determined by the traffic verification (encounters check). Those two verifications occur iteratively in order to ensure that both rules are fulfilled.

It is important to highlight that a 30 minutes spacing is recommended when hazardous cargo is involved. In the simulation model, this spacing is considered irrespective of the sequence of vessels involved in the process (assumption cited in paragraph 3.10). Considering that there are also Container and RoRo vessels calling at the port, it can be that neither the following nor the vessel sailing ahead carries hazardous cargo. In this case less spacing could be assumed between vessels. Approximately 3.5% of the waiting times due to spacing could be reduced. The number of cases at which the delay is overestimated is therefore considered insignificant and the same spacing is kept for all vessel combinations.

Berth availability

Once the incoming vessel exits the one-way sections, she either follow to her destination, or in case that the terminal is full, she waits at the inner anchorage until a berth is available.

When at the berth, the quay operations, which implementation is described later, start. Then, two hours prior to the vessel departure, a message is sent to the Harbour Master in order to claim for the one-way sections of the channel, and for high water when applicable. That is when the outgoing procedure starts.

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Vessels sailing 4.2.3

As already stated in the verbal model chapter, the sailing process is defined as a discrete process; the length of the channels and the vessel speed is used to obtain the time that it takes for the vessel to cross each section. The beginning and the end of the sailing process at each section is marked by events.

In order to enter the length of the channels to the model, a geo-referenced Nautical Chart was used as a base for tracing the vessel’s routes with the aid of CAD software. Segments with different speed limits were differentiated by colors (Appendix C). This drawing was then used as a FlexSim background and the NetworkNodes were placed at every channel divergence or convergence as well as in speed change locations. The nodes were then connected and the curved paths adjusted using splines. The distance between nodes was then verified for every single channel section.

Concerning the sailing speed of the vessels, the minimum value of the channel limit speed and the vessel class speed is taken. Table 4-4 presents the class related speeds.

Table 4-4 Vessel class related speed.

VESSEL CLASS SPEED (knots) BV01 12 BV02 12 BV03 12 BV04 12 BV05 10 BV06 10 BV07 10 TK01 10 TK02 10 CF01 12 CF02 12 CV01 10 RR01 15 LN01 10

Speed changes are performed on the triggers of the NetworkNodes. On the entrances of the one-way sections (S5 and S6/S7), a label keeps track of the speed of the slower vessel using the one-way sections. This speed limit is imposed by the NetworkNode to trailing vessels with higher speeds. Once the slower vessel leaves the one-way section a message is sent to the entrance NetworkNode in order to reset the limit speed. Vessels which are still at the channel do not change immediately their speed. They continue sailing with the reduced speed until the end of the one-way section.

The NetworkNodes, as well as other objects of the model are used to write information to Global Tables which are exported at the end of the simulation containing all data required for assessing the model results.

Quay operations 4.2.4

Once the vessel arrives at the berth, the time to be expended is calculated as described at the verbal model. The service time is estimated based on the amount of cargo to be handled and the berth productivity. The percentage of cargo to be handled is considered 100% for all classes except for TK01 and TK02 since they are only allowed to be loaded to a maximum draught of 14m. The input for berth productivities is presented in Appendix C. The vessel then stays at the berth during berthing, loading, unberthing and an additional time due to paperwork and others. The time for loading (service time) and the extra time are used to calculate the berth utilization rates. The berthing and unberthing times are not considered in this calculation.

49


As already stated, two hours prior to leaving the berth, the Harbour Master is contacted in order to give the clearance for leaving the port. When the quay operations are finished the vessel receives an “Outgoing” identification (the Outgoing label changes to 1) which is used by other model objects to distinguish incoming and outgoing vessels.

Model presentation 4.3

A view of the model with the Nautical Chart as a background is presented in Figure 4-3. Connections between objects, NetworkNodes, dummy queues, and other objects and names are not presented in order to make the image clearer. At the upper right corner, Vessel Generators are allocated as well as the Sink (entrance and exit of the port). Figure 4-4 presents a view of the terminals.

Figure 4-3 Simulation model view.

Figure 4-4 Terminals view.

50


5 SIMULATION MODEL EXPERIMENTAL SETUP

The just described simulation model was implemented in order to assess the performance of the marine operations of the Port of Jebel Dhanna Ruwais for the current (2014) and forecasted traffic (up to 2030). In this chapter the selection of scenarios to be simulated is given. First the traffic forecast is presented, and then the control variables and the sensitivity analysis are introduced. The selection of variables based on the sensitivity analysis and the composition of scenarios is then explained followed by some practical information on the experiments.

Traffic forecast 5.1

The traffic forecasts to be simulated are presented in Table 5-1. Year 2011 is used for the model validation. Year 2014 differs from year 2011 not only in the number of calls but it also includes the new GASCO Sulphur terminal, two extra container berths in BOROUGE and three new berths at the new jetty of TAKREER terminal. Year 2016 and 2022 are simulated since they correspond to the end of important construction phases of the ChemaWEyaat terminal which is already under construction. Therefore, the number of berths at the ChemaWEyaat terminal (destination 9 to 13) increases in time from 2016 to 2030 when the final configuration is reached (Table 5-2).

Table 5-1 Traffic forecast.

YEARS 2011 2014 2016 2022 2030 DESTINATIONS CALLS PER YEAR

1 Jebel Dhanna Terminal – ADCO (Crude oil) 120 120 120 120 120 2 Ruwais Refinery Terminal – TAKREER (Deep water) 450 695 930 930 930 3 Ruwais Refinery Terminal – TAKREER (Coastal tankers) 40 55 70 70 70 4 GASCO – Ruwais (LPG and Paraffinic Naphtha) 400 600 750 750 750 5 FERTIL – Ruwais (Urea, Ammonia, Ethylene) 110 100 100 100 100 6 GASCO – Ruwais (Sulphur) - 70 70 70 70 7 BOROUGE (Polyethylene) 520 260 260 260 260 8 GASCO – Ruwais (Sulphur) 2 - 70 70 70 70 9 ChemaWEyaat - Liquids and gas (excl LNG) - - 110 500 690

10 ChemaWEyaat - LNG and FRSU - - - - 52 11 ChemaWEyaat - Bulk - - - 30 30 12 ChemaWEyaat - Container - - - 80 440 13 ChemaWEyaat - Construction - - 50 50 50

TOTAL 1640 1970 2530 3030 3632

Table 5-2 Number of berths at the end of each phase of the ChemaWEyaat terminal construction.

TYPE OF BERTH PHASE 1 (2016) PHASE 3 (2022) PHASE 12 (2030) Liquids and gas (excl LNG) 3 5 10

LNG and FRSU - - 2 Bulk - 1 1

Container - 2 4 Construction 1 1 1

The distribution of vessel classes in each of the years is presented in Appendix C Figure C-2 Relative number of vessels per class. Table C-3 indicates the selection of one-way channel to be followed based on the minimum required tide of every vessel class. The distribution of vessels between the channels is the same for all years given that no changes in depth are considered and that the decision is made based on draughts for ballast and laden conditions. Additionally, for all simulated years, only vessel class BV01 has a Pilotage Exemption Certificate and can use the Relief Route for entering the port.

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Definition of control variables 5.2

There are three variables that have to be defined in order to perform a simulation: the traffic to be simulated, the spacing (in minutes) between vessels sailing in convoy, and the type of service time to be used. The values that can be attributed to these variables are presented in Table 5-3.

Table 5-3 Control variable and possible values to be taken.

CONTROL VARIABLE RANGE OF VALUES SimulationTraffic year 2011 2014 2016 2022 2030

Spacing minutes 0 15 30 ServiceTimeType - 0 1 2 3

The SimulationTraffic is defined for each scenario based on the year to be simulated. The Spacing and the ServiceTimeType are selected for each scenario based on the sensitivity analysis results.

The Spacing value of 0 simulates the condition that vessels do not keep a distance from each other when sailing in convoy. In this situation they are not allowed to overtake and they sail with the speed of the slowest vessels separated by the interval between their arrivals at the beginning of the one-way sections. Which means that if vessels arrive at the same time at the entrance of the one-way section they will sail together without any spacing between them. The reason why this hypothetical situation is simulated is that if no differences are found between this and the 15 and 30 Spacing values, the implementation of the spacing regulation can be done with the standard Non-passing FlexSim NetworkNode option, reducing considerably the programing time and effort.

Regarding the ServiceTimeType each value functions as follows:

0) The service time is calculated based on the total load of the vessels times the percentage of cargo to be handled (which is 100% for all vessels except the ones that are laden up to the maximum allowed draught) divided by the productivity of the berth (Eq. 5.1). As a result, vessels of the same class calling at the same terminal have the same loading time.

st0 =pl × DWT

P

With: st0 = service time when ServiceTimeType is 0 DWT = deadweight tonnage pl = percentage of the total load to be handled P = productivity

(Eq. 5.1)

1) The service time is calculated in the same way as for type zero. However, now the productivity of the berth is considered to vary uniformly from 80% to 120% (Eq. 5.2). In practice the service time then varies uniformly from 0.83 to 1.25 times the service time as it is obtained in type 0.

st1 =pl × DWT

uniform(0.8,1.2) x P

With: st1 = service time when ServiceTimeType is 1

(Eq. 5.2)

2) The service time is equal to 2 hours and 10 minutes for all vessels. Two hours is the minimum time required for the priority of outgoing vessels functionality to be ensured and 10 minutes is just a safety margin for the model to work properly.

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3) The service time is calculated as an Erlang-k distribution (k=2 for all berths except the container ones for which k=3).

st3 = erlang(0, (st0 k⁄ ), k) With: st3 = service time when ServiceTimeType is 3

(Eq. 5.3)

Sensitivity analysis 5.3

In this section the influence of the control variables is assessed in order to define the scenarios to be simulated. Model results are presented for this purpose only; therefore, no discussion on absolute result values is given. The assessment of the marine operations performance is addressed later in a dedicated chapter.

The sensitivity analysis was performed for the year 2030 given that it includes all berths and has the highest traffic. In order to estimate the influence of the Spacing and the ServiceTimeType on the assessment of the marine operations performance six scenarios were stablished (Table 5-4).

Table 5-4 Sensitivity analysis scenarios.

CONTROL VARIABLE SCENARIO

1 2 3 4 5 6 SimulationTraffic year 2030 2030 2030 2030 2030 2030

Spacing minutes 0 15 30 30 30 30 ServiceTimeType - 0 0 0 1 2 3

The variables to be compared between the six scenarios are:

total turnaround time; marine operations turnaround time; total waiting time; waiting time due to marine operations only; average waiting times per cause of delay; number of vessels at the outer and inner anchorage; number of pilots and tugs.

Figure 5-1 and Figure 5-2 present respectively the cumulative distribution and the histogram of turnaround time. From scenario 1 to 3 almost no differences can be observed therefore the total turnaround time seems to be not sensitive to the spacing regulation.

Scenarios 4 and 6 which count with a more variable service time than scenario 3 have smoother curves than the last one and scenario 5 has shorter and almost constant turnaround times given its fixed service time duration. The total turnaround time is hence very sensitive to the service time. However, when looking at the turnaround time due to marine operations only, a different conclusion is obtained. Figure 5-3 and Figure 5-4 show that the marine operations turnaround time is sensitive to the spacing and is not sensitive to the service time. The reason for the lack of influence of the service time is the absence of the waiting time due to berth unavailability in the marine operations turnaround time. The berth unavailability is the only cause of delay, in this case study, that is influenced by the service time. The reason why this hypothesis is not discarded from the beginning and is tested during the sensitivity analysis is that the different service times at different berths could change the order of (and interval between) outgoing vessels and therefore indirectly influence other causes of delay.

Important conclusions regarding the service time can be drawn based on this information. The marine operations are not sensitive to the service time. Therefore KPIs such as waiting time over turnaround time or service time are not representative for this specific performance evaluation.

53


However, they can still be useful in assisting the interpretation of the performance of the port as a whole.

Figure 5-1 Cumulative distribution of total turnaround time.

Figure 5-2 Histogram of total turnaround time.

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Figure 5-3 Cumulative distribution of marine operations turnaround time.

Figure 5-4 Histogram of marine operations turnaround time.

Same conclusions can be drawn from Figure 5-5 to Figure 5-8 which show the cumulative distributions and histograms of total waiting time and marine operations waiting time. Additionally, it can be concluded from the percentage of vessels without delay which is presented together with the histograms that this value is sensitive to the spacing.

55


Despite the fact that the waiting times due to the marine operations are not influenced by the service time, the percentage of vessels without any delay is slightly higher for scenario 5 for which all vessels have the same service time. It might be that the number of delayed vessels is reduced because vessels enter and leave the port with more or less the same order and interval between vessels. In other words, the sequence of incoming and outgoing vessels is less altered then with the other service time types.

Figure 5-5 Cumulative distribution of total waiting time.

Figure 5-6 Histogram of total waiting time for all vessels.

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Figure 5-7 Cumulative distribution of marine operations waiting time.

Figure 5-8 Histogram of marine operations waiting time for all vessels.

Figure 5-9 presents the average waiting time and the share corresponding to each source of delay. The same hold for Figure 5-10 however for the marine operations only thus, excluding berth unavailability waiting times.

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Figure 5-9 Average waiting times per cause of delay.

Looking at the three upper plots of Figure 5-9, it can be noticed that with increasing spacing not only the spacing share of the total waiting time increases but also the traffic share since due to the increase in the interval between vessels the one-way sections are occupied by outgoing vessels during a longer period.

Regarding the service time type, it can be observed that service time type 2 has almost no waiting time due to berth unavailability due to its very fast service time. Types 0 and 1 have nearly the same shares distribution and type 3, which adds stochasticity to the service time, have different proportions. However, when looking at the marine operations causes of delay only (Figure 5-10), different service time types give nearly the same results both in terms of average waiting time and proportions of causes of delay, reinforcing the conclusion that the service time type does not affect the marine operations.

Figure 5-11 and Figure 5-12 present the number of vessels at the outer and inner anchorages respectively. Both anchorages are influenced by the spacing. The service time only influences the inner anchorage since it is where the waiting for the berth occurs. The outer anchorage is not affected since only marine operations causes of waiting can lead to waiting times at the outer anchorage.

From Figure 5-13 and Figure 5-14 it can be concluded that the estimation of number of pilots and tugs is influenced both by the spacing and the service time. The influence of the service time can be explained due to the fact that for destination 1, one tug has to stay with the vessels moored to the SPM during loading operations. Additionally, pilots board at the Ghasha buoy and due to the simplifications of the estimative of number of pilots they do not disembark until they get to the berth irrespective of the time that they have to wait for the berth to be free in case of berth unavailability. When looking at the number of tugs, scenarios 3, 4 and 6 are equivalent.

58


Figure 5-10 Average waiting times per cause of delay – marine operations only.

Figure 5-11 Histogram of number of vessels at the outer anchorage.

59


Figure 5-12 Histogram of number of vessels at the inner anchorage.

Figure 5-13 Histogram of number of pilots.

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Figure 5-14 Histogram of number of tugs.

The conclusions of the sensitivity analysis are:

The spacing variable influences all evaluated model results. Therefore, the spacing of 30 minutes which is assumed due to the hazardous cargo transport is used for all scenarios.

The spacing regulation has to be customized and well defined in order to avoid an overestimation of the access channel capacity.

The service time has a large effect on the total turnaround time and waiting time. However, it does not affect results which are related exclusively to the marine operations.

Since the marine operations are not affected by the service time type but the estimation of pilots and tugs is, service time type 3 (Scenario 5) is discarded. However, when the estimation of number of pilots and tugs is not a requirement of the model the use of such type of service time input (fixed and short duration) should be evaluated given that it reduces significantly the amount of input information.

KPIs such as waiting time over turnaround time or service time are not representative for the marine operations performance evaluation. However, they can still be useful in assisting the interpretation of the performance of the port as a whole.

Provided that there is not enough information for performing a calibration of the service time, the type of service time to be used for the following simulation is type 0 (Scenario 3). This choice is made based on the fact that the difference between scenarios 3, 4 and 6 is not significant for any result related to the marine operations except to the number of pilots. However, since there are many other assumptions in the estimation of the number of pilots, the increase in the level of detail of the service time (without any calibration result support) does not increase the accuracy of this prediction.

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Scenario description 5.4

Based on the results of the sensitivity analysis the scenarios to be simulated are defined (Table 5-5). Results of scenario 1 are compared to Automatic Identification System (AIS) data in order to validate the model. Scenarios 2 to 5 are simulated in order to evaluate the performance of the marine operations and investigate the development of congestion with increasing traffic and new port developments.

Table 5-5 Scenarios definition


1 2 3 4 5 SimulationTraffic year 2011 2014 2016 2022 2030

Spacing minutes 30 30 30 30 30 ServiceTimeType - 0 0 0 0 0

Simulation duration, warmup and number of runs 5.5

Every scenario is run for one year (524160 minutes) in order to obtain yearly based statistics. At the start of the simulation the port is empty which does not represent the reality for every 1st of January. Therefore a warmup time is required in order to account for the vessels which are already inside the port in the beginning of the year. This warmup time is specially needed for this simulation model since the clearance for vessels to entering the port depends on the traffic flow of outgoing vessels at the one-way sections of the channel.

The duration of the warmup time was stablished by running the simulation model and observing the time that it takes for at least one vessel calling at every destination to enter the port, be served and leave. In approximately seventeen days this condition is met, therefore the warmup time should be at least of 17 days. Three weeks are therefore assumed as the warmup time, and are included before the start of the year (total simulation time of 554400 minutes). All statistics are derived for vessels that enter the model after 21 days.

Since the arrival of vessels at the port is simulated as random process based on a distribution, every single run executed with a different stream (or sequence of random numbers) gives a different result. Therefore, in order to derive significant statistics from the model, a certain number of repetitions are required. The number of runs necessary for obtaining results within a certain confidence interval can be estimated based on the standard deviation and the desired accuracy and confidence interval (Eq. 5.4).

N =

�σ × Zα2��2

d2 (Eq. 5.4)

With: N σ

Zα2�

d

Number of runs Standard deviation Two-tailed Z-score for a level of confidence 1-α Accuracy

A list of variables used for determining the number of runs is presented in Table 5-6. Also based on the sensitivity analysis, all variables related to the quay operations such as terminal utilization, service time and berth unavailability waiting times are not considered.

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Table 5-6 Variables considered for the number of runs definition.

VARIABLE UNIT Total Vessels that Leaved the Port vessels Total Vessels that Entered the Port vessels Arrivals to Jebel Dhanna - ADCO vessels Arrivals to TAKREER - Deep Water vessels Arrivals to TAKREER - Coastal Berths vessels Arrivals to GASCO - Ruwais vessels Arrivals to Fertil - Ruwais vessels Arrivals to GASCO - Sulphur vessels Arrivals to BOROUGE vessels Arrivals to GASCO - Sulphur 2 vessels Arrivals to CheemaWEyaat - Bulk vessels Arrivals to CheemaWEyaat - Liquid Bulk vessels Arrivals to CheemaWEyaat - LNG vessels Arrivals to CheemaWEyaat - Container vessels Arrivals to CheemaWEyaat - Construction vessels Outer Anchorage Average Content - Traffic vessels Outer Anchorage Average Content - Spacing vessels Inner Anchorage Average Content - Tidal window vessels Inner Anchorage Average Content - Spacing vessels Average Waiting time - OA - Traffic minutes Average Waiting time - OA - Spacing minutes Average Waiting time - IA - Tide minutes Average Waiting time - IA - Spacing minutes

The standard deviations were obtained by performing 40 runs for each scenario. Figure 5-15 and Table 5-7 show an example of the FlexSim replication plot and data summary corresponding to the average waiting time due to the traffic when performing 40 runs.

The confidence interval considered for the calculations is 5%, therefore the 𝑍𝛼2�

value is equal to 1.96. The accuracy was determined taking into account the feasibility study phase and the mean and variance of each variable. The resulted required number of runs for this case study is 20. The standard deviations obtained when running 20 times were reanalyzed and minor differences were encountered. Nevertheless, the required number of runs has remained unaltered, reaffirming that 20 runs are sufficient.

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Figure 5-15 Fourty replications plot. Total vessels arrivals.

Table 5-7 Total vessels that leaved the port – 40 replications data summary.

AVERAGE WAITING TIME DUE TO TRAFFIC RESTRICTION - OUTER ANCHORAGE (minutes) Scenario Mean (90% Confidence) Sample Std Dev Min Max

1 58.03 < 58.68 < 59.34 2.44 53.86 65.69 2 66.81 < 67.56 < 68.31 2.80 62.20 73.82 3 76.45 < 77.24 < 78.03 2.93 71.84 84.52 4 88.36 < 89.38 < 90.40 3.80 81.45 97.77 5 132.38 < 134.34 < 136.29 7.29 117.88 148.53

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6 SIMULATION MODEL VERIFICATION AND VALIDATION

The verifications of the model are performed by analysing data from the All Vessels Data output table which is presented in Appendix D. Only functions that were customized in order to implement the model are verified. For instance, the generation of vessels by the sources (vessels arrival) and the quay operations durations are not verified since they correspond to FlexSim default functionalities. Therefore, the verifications to be performed are concerning the regulations at the one-way sections (encounters and spacing) and the depth restrictions (routing of vessels and tidal window).

After verifying all model functionalities the simulation model is validated by comparing the output with AIS data.

One-way sections regulation and route assignment 6.1

The functionalities to be verified in this paragraph are vessels routing, traffic rules in the one-way sections (prevention of encounters and the spacing regulation), and tidal window.

However, first of all, a main verification has to be cited. In order to guarantee the priority of outgoing vessels, once they request for leaving the port their sailing times at the one-way sections have to be predicted and the channel blocked for incoming vessels during that period. The estimation of sailing times was verified for all situations irrespective of the followed route and destination by storing the predicted timetables and comparing them to real time stored data. For instance, the estimated arrival time at the entrance of S6/S7, was compared to the actual time that the vessel gets to the entrance of S6/S7 after finishing the quay operations and sailing out of berth. All sailing times were correctly estimated. In order to have the predicted times right, all distances, speeds and consequently sailing times should be represented correctly in the model therefore; not only the timetables but also the sailing process is considered verified.

Vessels routing

The routing of vessels through the Relief Route, Deep Water Channel or Stewart Channel can be verified by checking the output All Vessels Data table. By comparing the vessel class, the sailed route in and the sailed route out with the initial DW_in, DW_out and Pilotage Exemption Certificate information it is possible to verify if vessels took the right track.

The verification for the Relief Route is straightforward. Only exempted vessels are allowed to be there. For non-exempted outgoing vessels, DW_out 1 sails through the Deep Water and DW_out 0 through the Stewart. For non-exempted incoming vessels the verification is slightly different since many vessel classes can use either the Stewart or the Deep Water Channel. Therefore the only condition that is checked is that the ones that cannot enter the port via the Stewart Channel (the shallowest channel) should have sailed through the Deep Water channel. The same procedure was applied every time a new scenario was tested and the model was verified for all situations.

Encounters check

The encounters regulation is verified by checking the channel occupation tracked variables. Six channel occupation tracked variables are stored by the model. Every time a vessel enters section S5, S6 or S7 the occupation variable of that section is incremented by one. When the vessel leaves, the variable is reduced by one. Therefore, the number of vessels using each section can be accessed at every moment during the simulation. Separate variables are stored for incoming and outgoing vessels. Consequently, the encounters rule can be verified by checking if at every instant incoming vessels and outgoing vessels are not using the same section at the same time. This is done by multiplying the Incoming Occupation and the Outgoing Occupation variables of every section. The result has to be zero for every instant. Otherwise, two vessels in opposite directions are using that channel section. This verification can be observed in Figure 6-1. A 100 day timeseries of the Incoming

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(positive) and Outgoing (negative) occupation values for S6 – the Deep Water Channel is plotted every 10 minutes. The black “x” is the multiplication of incoming and outgoing vessels which are all in the y equal to zero line indicating that incoming and outgoing vessels do not encounter each other. The same holds for the entire simulation at every one-way section. Therefore this functionality is verified.

Figure 6-1 Incoming and outgoing occupation of S6 – timeseries.

The verification of model concerning the spacing functionality is now described.

Spacing regulation

Besides the encounters rule for vessels sailing in opposite directions, the spacing regulation is applied to the one-way channel sections. This model functionality is now checked.

The cumulative distribution functions of the spacing between vessels were derived at three key points of the one-way section for both incoming and outgoing vessels. The key points are: entry/exit of S6/S7, entry/exit of S5a and entry/exit of S5. Figure 6-2 and Figure 6-3 are two of the six generated graphs. A clear-cut can be noticed in both distributions at the 30 minutes spacing, meaning that all the values of spacing which are smaller than that are artificially turned into 30 minutes. However in Figure 6-2 a blue horizontal line is seen for values smaller than 30 minutes. When simulating 20 runs with a total of approx. 71000 vessels, intervals between vessels which are smaller than 30 have occurred in 0.036% of the cases (total for all one-way sections). The reason for that could not be identified. However this percentage is considered irrelevant for the model functioning and the spacing regulation is considered to be verified.

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Figure 6-2 Spacing verification for incoming vessels at the entrance of S5.

Figure 6-3 Spacing verification for outgoing vessels at the entrance of S6/S7.

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Tidal window

The tidal window functionality is verified by plotting the tide timeseries together with the moments at which the Tide Inner Anchorage has its content larger or equal to 1 and the moments at which a tidal bounded vessel is sailing at the depth restricted section. Figure 6-4 presents approximately six month of data for year 2014. This year was selected as only one value of minimum required tide is needed (1.3m see black dashed line) since in 2014 no CV01 vessels are calling yet. It is possible to notice that the red lines (which indicate that vessels are waiting for the tidal window to open) rarely cross the minimum required tide level. By zooming in (Figure 6-5) it can be noticed that sometimes there are vessels at the Tide Anchorage when the window is open, and that happens when the vessel is not allowed to start sailing since the tide is going to close before she exits the depth restricted section (see day 293). It can be observed that vessels are only sailing at the channel when the water level is above the 1.3m line. The very short times at which the vessels appear to be sailing when the tidal window closes (see day 296) are always less than 10 minutes (which is the model resolution) and the maximum difference between minimum required tide and the actual tide is 5 cm.

The Tide Anchorage is cleared immediately after the window opens irrespective of the number of vessels waiting. For instance, when two vessels are waiting for the tide, once the tidal window opens the first vessel in line enters the channel and the following vessels go to the spacing anchorage until the minimum distance in time between vessels is reached. At this moment the second vessel in line enters the channel and follows the previous one maintaining the spacing until the end of the one-way section.

Additionally, when running the model in real time it is possible to verify the tidal window looking at the sign that shows if the window is opened or not and for which “colours” of vessel. For instance, “orange” vessels can enter the channel when the tide is higher than 1.0m while “red” vessels can only enter the channel when the tide is as high as 1.3m. Therefore, if red and orange vessels arrived at the anchorage more or less at the same time, the orange vessel will leave earlier since her tidal window opens earlier.

Figure 6-4 Tidal window verification – 6 month.

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Figure 6-5 Tidal window verification – 28 days.

Now that all the model functionalities are verified the model can be validated.

Simulation model validation 6.2

The validation of the model is based on the traffic data of 2011. This selection is made due to the fact that the traffic data for 2011 and the other 4 simulated scenarios come from the same source. Additionally, performing the validation for year 2014 would be difficult because of the influence of new berths that are starting to be operated. Moreover, it would not be possible to use a complete year dataset of AIS information for the validation.

The validation data was obtained from the Sea-web´s Movements Database which consists primarily of AIS data and is supplemented by a number of other sources (Sea-web, 2014). Data for all commercial vessels (which excludes tugs, and other service crafts) calling at The Port of Jebel Dhanna/Ruwais in year 2011 were collected. For every vessel, the arrival date and time and the time spent at the port are given. Since no information is available at the website concerning the location from which the time at port is started to be counted for, the limits of the Jebel Dhanna/Ruwais Port, as established by the Port Authority, are considered. See red dashed line in Figure 6-6.

The turnaround time of the simulation model starts to be counted from the Outer Fairway; therefore, in order to validate the model the sailing time from the Outer Faiway to the Port Limits (Ghasha) was discounted from the total simulated turnaround time.

Very short turnaround times were observed in the Sea-web dataset. The cause of these very short turnaround times cannot be identified. Since the simulation model does not include unidentified processes that can lead to short turnaround times, the Sea-web data was filtered by excluding values smaller than the minimum calculated turnaround time.

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Figure 6-6 Port of Jebel Dhanna/Ruwais Limits (ADNOC - PPA, 2013).

It is important to highlight that there are many drawbacks of using the turnaround time for validating this simulation model. First of all, due to the high aggregation level of this variable, validating the turnaround time does not imply that every simulated process is validated. For the same reason, when differences are found between simulated and real data, it is not possible to identify their source.

Additionally, one of the main contributions to the turnaround time is the duration of the quay operations. Since the aim of the simulation model is to assess the performance of the marine operations, the quay operations are not reproduced in great detail. The approximation of a deterministic service time depending on the vessels class and berth of call may lead to different turnaround times than in reality.

Therefore, it would be recommended to perform the simulation model validation with the turnaround time of the marine operations only - excluding the service time, the extra time at berth and the waiting time due to berth unavailability as suggested in paragraph 3.9. However, since data are not available, the total simulated turnaround time will be evaluated compared to the Sea-web turnaround time.

Figure 6-7 presents the histogram of turnaround time for both simulated and Sea-web data.

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Figure 6-7 Histogram of simulated turnaround time, whithin port limits.

The differences between simulated and Sea-web data can be visualized in Figure 6-8 and Figure 6-9. The first one presents the difference between the simulated and the Sea-web histograms. The maximum differences are obtained for shorter turnaround times and are in the order of 15%.

Figure 6-8 Histogram differences (Simulated – Sea-web).

Attention should be given to the fact that the biggest difference corresponds to the first class of turnaround time. Since the source of very short turnaround times at the Sea-web data is not known, it can be that some of the vessels that should stay longer in the port according to the simulation

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model are also being affected by these unknown processes and in reality are leaving the port earlier than expected. If the identification of these processes was possible, maybe some of the vessels that fall into those first classes should also have been filtered out of the dataset.

Figure 6-9 Cumulative distributions of simulated and Sea-web data.

Regarding the cumulative distributions comparison, it can be noticed that the Sea-web curve is smoother than the simulated curve. As mentioned before, the fact that the service time is simulated as a deterministic value can be the cause of this difference. For instance, when looking at the histogram of service time for year 2011 (Figure 6-10), the step on the simulated cumulative distribution curve around the 80 hours turnaround time is certainly related to the peak in the histogram of service time at 80 hours.

Figure 6-10 Histogram of service time as a deterministic input (Scenario 1).

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Conclusions 6.3

In this chapter all model functionalities were successfully verified. The validation of the model shows that the model results correspond in pattern and order of magnitude with real data. The differences encountered are acceptable given the limitations of the validation procedure which are summarized in the sequence.

The service time is the most significant contribution to the turnaround time. This process is included in the model in a simplified way by assuming that all vessels are fully loaded and that the berth productivity is constant. Therefore, the use of the turnaround time of the marine operations only for the model validation would be advised. Nevertheless, by considering the simplification in the service time process when performing the validation, the encountered differences are better understood and the validation with the total turnaround time can be effectively performed.

The Sea-web dataset presents very short turnaround times which are an outcome of unidentified processes. Values smaller than the minimum possible turnaround in the simulation model were filtered out of the dataset; however, data from the remaining dataset can also be resultant from processes which are not included in the simulation model. This could explain the differences in turnaround time between modelled and real data for short turnaround times.

Given the model assumptions and the lack of data and consequently calibration of the service time, which is the most significant contribution to the turnaround time, the encountered differences are acceptable for this case study application. Therefore, the model is considered verified and validated for the application of this graduation work and the results are presented and analysed in the following chapter.

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7 ASSESSMENT OF THE MARINE OPERATIONS PERFORMANCE

This chapter presents the results of the simulations performed for the last four of the already described scenarios which correspond to the traffic of 2014, 2016, 2022 and 2030. The performance of the Port of Jebel/Dhanna Ruwais marine operations is discussed for all scenarios.

Simulation model results 7.1

The results required for assessing the Port of Jebel Dhanna/Ruwais marine operations are presented in this paragraph. Values presented in tables are average results. For information on 10%, 50% and 90% percentiles, box plots are presented.

Every time that the Marine Operations term is mentioned this implies that the quay operations and the waiting times due to berth unavailability are not considered; for instance, the marine operation turnaround time excludes the time spent at berth (service time + extra time) and the time spent waiting for the berth. Causes of delay related to the marine operations are therefore: traffic, spacing and tidal window.

When comparing results between scenarios it is important to keep in mind that not only the traffic is increasing but there are also differences in terms of number of berths and percentage of vessels of each vessel class (fleet mix). These differences can mask some results if not regarded carefully.

As discussed during the sensitivity analysis, the total turnaround time is highly affected by the choice of the service time. However, when looking only at the marine operations, there is hardly any influence of the service time. Therefore, KPIs such as waiting time over turnaround time or service time are not valuable for evaluating the performance of the marine operations. However, the total turnaround time, the total waiting time, the waiting time due to berth unavailability over the service time are presented to assist the interpretation of differences between scenarios.

Focus should be given to the marine operations causes of delay and to the waiting time over marine operations turnaround time as indicators of the Port of Jebel Dhanna / Ruwais marine operations performance.

In order to give a first impression of the results, histograms of the total waiting time and the marine operations waiting times are presented in the beginning of the results discussions. Figure 7-1 shows that the percentage of vessels with less than 30 minutes of delay, for both total and marine operations waiting times, decreases from 2014 to 2030. It is possible to notice that the marine operations waiting time have less extremely high waiting times than the total. Therefore, it can already be concluded that the berth unavailability cause of delay contribution is substantial.

The above mentioned histograms include non-delayed vessels. On the contrary, all other waiting time results are presented for delayed vessels only, meaning that the statistical analysis excludes waiting times equal to zero. Therefore, the waiting time information should be interpreted together with the percentage of delayed vessels. Additionally, the percentage of delayed vessels is calculated taking into account only the vessels which are subject to that delay. For instance, all vessels can have berth unavailability delays; however, the marine operations delays can only occur for vessels using the Main Access and not to those using the Relief Route. Similarly, the tidal window delay can only happen to tidal bounded vessels. In the case of tidal window delays, percentage results are presented both w.r.t. all vessels using the Main Access and w.r.t. tidal bounded vessels only.

The results presented are percentage of delayed vessels and average waiting times (7.1.1); number of vessels at the anchorages (7.1.2); KPIs evaluation (7.1.3); number of served vessels and cargo throughput (7.1.4) and number of pilots and tugs (7.1.5).

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Figure 7-1 Histograms of total and marine operations waiting times.

Percentage of delayed vessels and average waiting times 7.1.1

The percentage of delayed vessels and the average waiting times for both all causes and marine operations only are presented in Table 7-1. It can be noticed that the percentage of vessels with any delay is increasing over the years. The average waiting time of those delayed vessels is also increasing; however, year 2016 presents an even higher average waiting time than 2030. The reason for that can only be identified when looking at the causes of delay further in this report.

Table 7-1 Percentage of delayed vessels and average total waiting times for delayed vessels.

OUTPUT (AVERAGE VALUES) 2014 2016 2022 2030 Delayed Vessels - All Causes (%) 65.3% 75.9% 76.3% 83.0%

Total Waiting Time - WT (hours) 7.33 12.31 9.24 9.57 Delayed Vessels - Marine Operations (%) 57.1% 65.1% 70.6% 79.9%

Waiting Time - Marine Operations - WTMO (hours) 3.54 3.25 3.08 3.72

When looking to all causes of delay related to the marine operations only, a growth in the percentage of delayed vessels with the years can be noticed. However, the average waiting times caused by the marine operations is only slightly fluctuating (Figure 7-2). From this simple segregation between all causes of delay and marine operations sources only, it can already be observed that the main cause of delay might be the berth unavailability.

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Figure 7-2 All causes and marine operations only waiting time box plots.

Table 7-2 show the contribution of each source of delay to the total waiting times. Since the average waiting times given the cause of delay cannot be directly added up to estimate the total delay due to the marine operations, a distinction between delays for incoming vessels and outgoing vessels considering the marine operations causes of delay is done. The percentage of delayed vessels and the average waiting times due to traffic and spacing for incoming vessels and tide and spacing for outgoing vessels is therefore given.

Table 7-2 Percentage of delayed vessels and waiting times per cause of delay.

YEAR OUTPUT (PER CAUSE OF

DELAY) AVERAGE VALUES

OUTER ANCHORAGE INNER ANCHORAGE

ONLY SPACING

ONLY TRAFFIC

TRAFFIC AND

SPACING BERTH ONLY

SPACING ONLY TIDE *

TIDE AND SPACING

2014 Delayed Vessels (%)

11.6% 22.0% 5.0% 17.0% 14.1% 10.8% 5.6% 2016 14.0% 25.6% 8.5% 28.3% 18.1% 9.2% 6.0% 2022 15.7% 27.9% 11.4% 24.7% 21.1% 8.8% 5.2% 2030 17.1% 29.1% 20.0% 24.0% 25.7% 11.0% 5.5% 2014

Waiting Time - wt (hours)

0.33 1.12 1.76 16.31 0.60 10.60 7.61 2016 0.37 1.28 2.02 25.56 0.60 10.80 7.67 2022 0.40 1.47 2.26 20.54 0.62 10.42 7.57 2030 0.51 2.14 3.44 21.58 0.66 9.13 7.27

* w.r.t. all vessels

It is noticed that with increasing number of vessels, the percentage of vessels that have to wait for the traffic, spacing and for both traffic and spacing also increases.

Correlation plots between waiting times due to traffic and spacing and between waiting times due tidal window and spacing for incoming and outgoing vessels respectively are presented in Appendix E. The plots corresponding for years 2014 to 2030 can be observed in Figure E-1. Even though an increase in both traffic and spacing percentage of delayed vessels and waiting times occurs the correlation between these variables is very week for the simulated traffic.

For outgoing vessels, since not every vessel is subject to the tidal window, the percentage of vessels delayed due to the spacing regulation increases but the percentage of vessels waiting for the tide or

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both the tide and the spacing does not change significantly. As expected from the table data analysis, no correlation can be seen between tide and spacing (Figure E-1 bottom plots).

Figure 7-3 shows that for incoming vessels, both traffic and spacing waiting times are increasing from 2014 to 2030.

Figure 7-3 Traffic and spacing (incoming vessels) waiting time box plots.

In case of outgoing vessels, even though the percentage of delayed vessels due to spacing is increasing, the waiting times related to this cause of delay do not increase as much as for incoming vessels (Figure 7-4).

Figure 7-4 Berth unavailability, spacing (outgoing vessels) and tidal-window waiting time box plots.

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Figure 7-5 Average waiting time per cause of delay.

In Figure 7-5, it can be noticed that despite of the increase in the marine operations causes of delay contribution, the berth unavailability correspond to the greatest share of the average waiting times. The sum of all marine operations causes of delay corresponds to less than 50% of the average waiting times for all simulated years.

The reason why the average waiting time is increasing from 2014 to 2030 despite the fact that a new terminal is being developed and new berths are becoming available is explained further on this section.

With respect to the tidal window percentage of delayed vessels and average waiting times, Table 7-2 should be regarded together with Table 7-3.

Table 7-3 Percentage of tidal bounded vessels and classes distribution.

TIDAL BOUNDED VESSELS 2014 2016 2022 2030 Percentage of Delayed Vessels

(w.r.t. all vessels) All Classes 16.33% 15.23% 13.64% 16.09%

Percentage of Tidal Bounded Vessels (w.r.t. all vessels)

All Classes 21.6% 20.3% 18.7% 24.7% CV01 (1.0m) - - 1.4% 10.5% TK01 (1.3m) 13.5% 13.6% 11.8% 9.6% TK02 (1.3m) 8.1% 6.7% 5.5% 4.7%

Percentage of Delayed Vessels (w.r.t. tidal bounded classes)

All Classes 75.7% 75.1% 73.1% 65.1% CV01 (1.0m) - - 50.3% 50.1% TK01 (1.3m) 75.6% 75.2% 75.1% 76.3% TK02 (1.3m) 75.9% 74.9% 74.4% 75.7%

It is noticed that the percentage of delayed vessels due to the tidal window w.r.t. all vessels decreases in 2016 and 2022 if compared to 2014 (Table 7-3). This is due to the fact that the percentage of tidal bounded vessels with respect to all vessels also decreases for these two

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scenarios. Additionally, when looking at year 2030, the percentage of tidal bounded vessels with respect to all vessels is higher than in 2014. However, the most representative class to the tidal bounded vessels is CV01 (10.5% of all vessels) which requires 1.0m of tide (MSL = 1.1m). Therefore, the relative number of delayed vessels due to the tide decreases (as well as the average waiting time). But when looking at the percentage of delayed vessels of classes TK01 and TK02 with respect to its own classes the situation does not vary considerably from one year to the other (approx. 76% of the vessels from classes TK01 and TK02 are delayed due to the tidal window).

Within the marine operation causes of delay, the most significant in terms of waiting times is the tidal window cause (Figure 7-6), however not all vessels are affected by this regulation. The second main marine operation cause of delay is the traffic in the one-way sections of the channel which will affect almost half of all calling vessels that do not sail through the Relief Route in 2030. However, waiting times related to this regulation are not extremely high. Later on this report the waiting times are evaluated with respect to the marine operations turnaround time in order to better quantify the significance of each cause of delay (7.1.3).

Regarding the berth unavailability cause of delay, 2016 corresponds to the year with the greatest percentage of waiting vessels among the simulated scenarios. The reason for that is the increase in traffic from 2014 to 2016 in two terminals with already high occupancies in 2014 (Table 7-4). Although the berth occupancy is not directly related to the marine operations of the port, it is important to keep track of it in order to understand the congestion evolution of the port as a whole.

Figure 7-6 Average value and proportions of waiting time per cause of delay – Marine Operations.

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Table 7-4 Terminal occupancy rates.

DESTINATION TERMINAL OCCUPANCY RATE - BO

2014 2016 2022 2030 1 Jebel Dhanna Terminal – ADCO (Crude oil) 14.1% 14.2% 13.9% 14.2% 2 Ruwais Refinery Terminal – TAKREER (Deep water) 44.1% 57.7% 59.3% 58.4% 3 Ruwais Refinery Terminal – TAKREER (Coastal tankers) 2.9% 4.0% 3.8% 4.2% 4 GASCO – Ruwais (LPG and Paraffinic Naphtha) 60.9% 76.6% 75.3% 75.5% 5 FERTIL – Ruwais (Urea, Ammonia, Ethylene) 49.5% 50.9% 49.9% 50.1% 6 GASCO – Ruwais (Sulphur) 10.1% 10.4% 10.0% 10.7% 7 BOROUGE (Polyethylene) 16.5% 16.6% 16.5% 16.3% 8 GASCO – Ruwais (Sulphur) 2 10.0% 10.5% 9.4% 10.4% 9 ChemaWEyaat - Liquids and gas (excl LNG) - 3.9% 11.5% 19.4%

10 ChemaWEyaat - LNG and FRSU - - - 15.3% 11 ChemaWEyaat – Bulk - - 11.8% 12.6% 12 ChemaWEyaat - Container - - 8.3% 62.5% 13 ChemaWEyaat - Construction - 7.4% 7.4% 7.3%

The TAKREER terminal (destination 2) has an increase in 235 calls from 2014 to 2016 and its occupancy rate goes from 44.1% in 2014 to 57.7% in 2016. The GASCO terminal for LPG and Paraffinic Naphtha (destination 4) has an increase in 150 calls per from 2014 to 2016 and its occupancy goes up from 60.9% in 2014 to 76.6% in 2016. In the following years nothing changes in both destinations; therefore, the percentage of waiting vessels calling at destinations 2 and 4 will not decrease. However, since new berths start to operate in 2022 and 2030, and the utilization rates of most of the recently inaugurated destinations are low, the percentage of waiting vessels due to berth unavailability decreases from 2016 to 2030 ending up with 7 percentage points higher than in 2014. Nevertheless, there are still terminals operating with a high occupancy rate even amongst the newly developed ones. In order to reduce the percentage of waiting vessels and the waiting times due to berth unavailability, alternatives for reducing these occupancies such as increasing loading rates or distributing vessels over other terminals (when possible) should be evaluated. This is out of the scope of the graduation work. Nevertheless, if the representation of the quay operations processes in the simulation model is improved, the alternatives for reducing the berth unavailability source of delay can be implemented in the simulation model and its extended version can be used for identifying the most effective measure.

From these first presented results it can be concluded that the number of vessels delayed due to the traffic and spacing increases, and so does the contribution of these causes of delay to the total waiting times. The percentage of vessels delayed due to the tidal window w.r.t. all vessels and the corresponding average waiting time are slightly reduced due to the increase in tidal bounded vessels that require less tide to sail through the depth restricted sections. The average waiting time due to marine operations is not significantly altered from 2014 to 2030. Even though new terminals are being developed, the berth unavailability continues to be the greatest source of delay, contributing to an increase in the total average waiting time.

Number of vessels at the anchorages 7.1.2

Regarding the number of vessels at the anchorages, Figure 7-7 and Figure 7-8 present the cumulative distribution function of total number of vessels waiting at the outer and inner anchorages respectively. It can be observed that in 2014, 90% of the time the outer anchorage is empty, in 2016 and 2022 the outer anchorage has one or less vessels and in 2030 two or less vessels. With respect to the inner anchorage, in 2014, 90% of the time three or less vessels are waiting, in 2016 five or less and in 2022 and 2030 six or less.

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Figure 7-7 Cumulative distribution of vessels at the Outer Anchorage.

Figure 7-8 Cumulative distribution of vessels at the Inner Anchorage.

Figure 7-9 and Figure 7-10 present the histograms corresponding to the previous cumulative distribution functions and Figure 7-11 and Figure 7-12 divide the number of vessels at the anchorages according to the reason of waiting.

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Figure 7-9 Histogram of number of vessels at the Outer Anchorage.

Figure 7-10 Histogram of number of vessels at the Inner Anchorage.

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Figure 7-11 Number of vessels at the anchorages due to marine operations sources of delay.

It can be noticed that the main contribution to number of vessels at the outer anchorage is the traffic and at the inner anchorage is the berth unavailability followed by the tidal window.

Figure 7-12 Number of vessels at the anchorage due to berth unavalability.

Until now, the percentage of waiting vessels, the waiting times and the number of vessels at the anchorages were evaluated. In order to assess if the waiting times are acceptable or not, the KPIs are now discussed.

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KPIs evaluation 7.1.3

In the model output requirements paragraph (3.9) three key performance indicators were introduced.

I. Waiting time / turnaround time; II. Waiting time / service time;

III. Berth occupancy.

Following the sensitivity analysis reasoning, the turnaround time and the waiting time to be used in the calculation of KPI I are the ones related to the marine operations only. Since the service system that is being evaluated corresponds to the navigational services to vessels, the marine operations turnaround time can be regarded as the “service time” of the navigational service system. However, in order to avoid confusion with the well-known service time related to the cargo handling services, the navigational services service time is referred to as marine operations turnaround time.

Typically accepted values for the waiting rate vary between 10% for container vessels to 30% for bulk vessels (PIANC, 2014b). Since the majority of vessels are bulk vessels, the average results will be compared to 30%. However, it is not straightforward to analyze this KPI since for customers (the calling vessels) the main service provided by the port is the cargo handling services, so no waiting time due to the marine operations at all would be the ideal situation. However, if the vessel has to wait for the berth anyways it does not matter if she is taking longer to cross the access channel or not. Therefore, once the waiting due to marine operations start to prevail over the berth unavailability waiting, its performance can be considered unacceptable.

Regarding KPI II, only results for the berth unavailability cause of delay are presented. The berth occupancy was already presented in the previous chapter in order to understand the increase in waiting times from 2014 to 2016. It will not be discussed separately here since it does not give an indication of the marine operations performance itself but assists the evaluation of the port congestion.

The average service time, the total turnaround times, the marine operations turnaround time and KPI I for marine operations only are given in Table 7-5. Table 7-6 presents the same KPIs but separated by the cause of delay. For 10%, 50% and 90% percentiles of variables presented in tables please refer to Figure 7-13 to Figure 7-16.

Table 7-5 Average turnaround time, service time and average waiting time over turnaround time.

Output (Average values) 2014 2016 2022 2030 Total Turnaround Time - TR (hours) 48.7 52.6 48.8 52.6

Turnaround Time (Marine Operations) - TRMO (hours) 15.5 15.6 15.5 16.4 Service Time - st (hours) 27.7 27.0 25.6 28.4

Waiting Time /Turnaround Time (Marine Operations) - KPI1𝑀𝑂 % 15.4% 14.6% 14.3% 17.1%

Table 7-6 Average KPIs per cause of delay.

YEAR OUTPUT (PER CAUSE OF DELAY)


ONLY SPACING

ONLY TRAFFIC

TRAFFIC AND

SPACING BERTH ONLY

SPACING ONLY TIDE

TIDE AND

SPACING 2014 Waiting Time /

Turnaround Time (Marine Operations) 𝐊𝐏𝐈𝟏𝐌𝐎 per cause

2.36% 6.72% 10.59% - 3.92% 38.95% 31.28% 2016 2.57% 7.61% 11.81% - 3.88% 39.18% 31.10% 2022 2.78% 8.59% 12.97% - 3.99% 37.70% 30.64% 2030 3.39% 11.24% 17.57% - 4.09% 33.28% 28.72% 2014

Waiting Time/ Service Time

𝐊𝐏𝐈𝟐

- - - 67.61% - - - 2016 - - - 124.64% - - - 2022 - - - 99.06% - - - 2030 - - - 97.19% - - -

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It can be observed that the “service time” of the navigational services to vessels is approx. half of the service time of the cargo handling services. However, the waiting rates for the later are much higher than for the former. It is important to recall that the percentage of delayed vessels due to the berth unavailability in 2030 is 24% and due to the marine operations 80%. Thus, fewer vessels have to wait for the berth than for the marine operations, but when they do, they wait for a long time. As a reminder, results concerning berth unavailability and service time should not be considered absolute since not enough data was available for the calibration of quay operations.

Figure 7-13 Marine operations turnaround time and service time.

Figure 7-14 Waiting time over turnaround time for marine operations only.

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Figure 7-15 Berth unavailability waiting time over service time.

Figure 7-16 Waiting time over turanround time for marine operations only per cause of delay.

The tidal window is the marine operation cause of delay which has a waiting rate higher than 30%. However, the increase in traffic has barely any influence on the waiting times for tidal bounded vessels. The average waiting due to the tide is even decreased due to changes in the fleet mix as already explained in the previous chapter. The spacing for outgoing vessels (inner anchorage) is also not very much affected. The reason for that is the priority for outgoing vessels. It is clear that the traffic increase has much more influence on incoming vessels than on the outgoing ones. Both the traffic and the spacing for incoming vessels are increasing in terms of percentage of delayed vessels, average waiting time and average rate of waiting.

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Number of served vessels and cargo throughput 7.1.4

The number of vessels served by the port for every scenario and for every vessel class is presented in Table 7-7. The cargo throughput reached by every class is approximated by the dead weight tonnage or the TEU that can be carried by the vessel, times the percentage of cargo to be handled which is 100% for all vessels except for the deep draught tankers which are loaded until the maximum draught of 14m.

The number of served vessels is compared to the traffic forecast and the conclusion is that the port can handle the predicted traffic for all scenarios. Since the forecast is established based on the production of the industries situated at the port surroundings and that it is considered that all vessels are loaded to their maximum, the cargo throughput obtained during the simulations should also be sufficient for transporting the predicted production. This information can also be obtained for every calling terminal and the cargo throughput per product can also be assessed. However, for the simulated scenarios for which the traffic is an input, this information does not have other intuit than verifying if the input traffic can be handled. When the maximum throughput that a port can process is one of the questions to be answered by the model, these outputs become more relevant.

Table 7-7 Number of served vessels and respective cargo throughput.

CLASS SIZE % NUMBER OF SERVED VESSELS CARGO THROUGHPUT

2014 2016 2022 2030 2014 2016 2022 2030 UNIT BV01 3.000 DWT 100% - - 81 84 - - 243 252 k ton BV02 5.000 DWT 100% 20 43 94 97 100 215 470 485 k ton BV03 15.000 DWT 100% 103 174 294 299 1545 2610 4410 4485 k ton BV04 20.000 DWT 100% 196 254 268 298 3920 5080 5360 5960 k ton BV05 40.000 DWT 100% 310 453 591 703 12400 18120 23640 28120 k ton BV06 50.000 DWT 100% 335 423 414 475 16750 21150 20700 23750 k ton BV07 60.000 DWT 100% 319 416 416 413 19140 24960 24960 24780 k ton TK01 120.000 DWT 87,5% 267 347 358 349 32040 41640 42960 41880 k ton TK02 200.000 DWT 75,0% 159 170 167 172 31800 34000 33400 34400 k ton CF01 1.100 TEU 100% 195 196 195 193 214500 215600 214500 212300 TEU CF02 1.200 TEU 100% - - 42 58 - - 50400 69600 TEU CV01 5.000 TEU 100% - - 41 382 - - 205000 1910000 TEU RR01 9.000 DWT 100% 67 67 65 66 603 603 585 594 k ton LN01 90.000 DWT 100% - - - 54 - - - 4860 k ton

TOTAL 1971 2543 3026 3643 118,298 148,378 156,728 169,566 k ton TOTAL 214500 215600 469900 2191900 TEU

Pilots and Tugs 7.1.5

With all the assumption that were taken for the approximation of the required number of pilots, it is important to remind that the results are only indicative and do not take into account the shift hours neither the transfer time from one vessel to the next one. These results are intended to give an estimate only in order to identify if the delays due to lack of pilots should have been included in the model. It is known that at the moment 26 pilots are based at the Port of Jebel Dhanna/Ruwais. Therefore, based on Figure 7-17 and Figure 7-18 it is assumed that there is no need for including waiting for pilots’ delays to the simulated scenarios.

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Figure 7-17 Cumulative distribution of number of pilots.

Figure 7-18 Histogram of number of pilots.

The same description given for the pilots’ results holds for the number of tugs estimative. The number of tugs available at the Port of Jebel Dhanna/Ruwais is 9. Therefore, if there is any waiting for tugs, it should not be significant for the yearly statistics. However, this affirmation is only valid for the required level of detail of the result. In order to insure that this is actually the case, more details should be included in the model.

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Figure 7-19 Cumulative distribution of number of tugs.

Figure 7-20 Histogram of number of tugs.

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Conclusions 7.2

The purposes of implementing the simulation model was established in the verbal model chapter and are here transcribed to support the conclusions of the marine operations performance assessment. The following tasks were expected to be executed based on the model results.

To investigate the development of congestion with increasing traffic; To assess the performance of the port with increasing traffic; To identify bottlenecks (sources of delay); To assess the ability of the port to handle the forecasted traffic and throughput; Estimate the number of pilots and tugs needed to provide the services.

Based on the tables and figures presented in this chapter, it can be concluded that all the listed tasks can be properly executed supported by the simulation model results.

The results show that the the number of vessels delayed due to the traffic and spacing regulations increases with the increasing traffic, and so does the contribution of these causes of delay to the total waiting times.

The berth unavailability and the tidal window waiting times are highly dependent on the fleet mix and berth occupancy at the destination of call. Likewise, the start of operation of new berths cannot be disregarded when analysing results. In order to fully understand the changes from one scenario to the other, those factors should be always taken into consideration when making comparisons. For instance, the decrease in waiting times due to the tidal window occurs due to the fact that a new class of vessel which has a lower minimum required tide is introduced from 2022. The percentage of delayed tidal bounded vessels is also reduced; however, when looking separately to each class of tidal bounded vessel, the percentage and the average waiting times are maintained the same for the classes that were already present before 2022.

Although nearly all waiting times and percentage of delayed vessels are increasing, the berth unavailability is still the most significant source of delay. Despite the fact that not a lot of vessels are delayed due to the berth unavailability, the waiting times for this cause of delay are in the same order of magnitude as the service time. Since the model has significant simplifications in the quay operations and the objective is to evaluate the marine operations, the performance of terminals is not further evaluated. However, by doing proper modifications in the simulation model, an extended version of it could be used for optimizing the berth occupancy.

The waiting rate of the marine operations is still below the acceptable value (maximum of 17% of the marine operations turnaround time in 2030). The main cause of delay within the marine operations is the tidal window. Since the tide at the location is diurnal, waiting times can be very high.

However, since no changes are noticed in percentage of delayed vessels and waiting times within the same class with increasing traffic, the tidal window is not considered a significant bottleneck at least until 2030. Nevertheless, if a reduction in waiting times of the tidal bounded vessels is still desired, an optimization of the use of the tide is suggested. This optimization can be achieved by planning of arrival and departures of tidal bounded vessels and by controlling and restricting the loading operations based on the tidal level.

The marine operations of the Port of Jebel Dhanna/Ruwais are therefore considered to be appropriate for handling the forecasted traffic until 2030, in the scope of a feasibility study. No real bottleneck is identified for the simulated scenarios. However, since the results are very sensitive to the fleet mix, more information and monitoring of changes with respect to this input is advisable.

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8 COMPARISON TO HAND CALCULATIONS

It is known that hand calculations are much easier to perform than computer simulations; on the other hand, the ouputs of the first are also much simplier than the former. The selection between one approach or the other depends very much on the complexity of the system and the level of detail required for the outputs. In order to evaluate the added value of performing simulations for assessing marine operations performances, a brief comparison with some hand calculations is presented.

The utilization of the one-way sections of the access channel is obtained by calculation the time that vessels are going to be using the channel. Two simple but somewhat different methods are applied. The first one for all one-way sections, separated into incoming and outgoing vessels, and the second one related to the tidal window for which more details are included. The simplifications of both applied methods are:

1. Same speed for all classes of vessels (10knots); 2. For the estimative of incoming and outgoing vessels occupation, only one vessel is allowed at

each section at a time; 3. When calculating the utilization of the tidal window, 8knots is assumed for all tidal bounded

vessels; 4. The utilization of the tidal window is estimated for one vessel at the channel at a time, 15

minutes spacing and 30 minutes spacing (same as the simulation model); 5. The minimum required tide is assumed to be 1.3m even for the ones that require only 1.0m.

Assumption number one implies that vessels with higher speed would have spent less time at the channel, therefore it can result in higher values of occupation. Same holds for assumption two, considering that only one vessel can be at the section results in higher values of utilization.

Assumptions number 3 and the 30minutes calculation of assumption 4 are the same as for the simualtion model. Assumpiton number 5 implies that less time is available for tidal bounded vessels to sail than in the simulation model.

The most relevant difference between the hand calculations and the simulation model is the selection of the route to follow. In the hand calculations if the vessel can sail through the Stewart Channel (S7) she will irrespective of the outgoing traffic. In the simulation model, vessels that are allowed to use the Stewart Channel can also sail through the Deep Water Channel (S6) and they will do that in case an encounter is predicted to happen if sailing through S7 and not through S6. For outgoing vessels, the route selection is performed in the same way for both simualtion model and hand calculations.

Hand calculation results 8.1

Figure 8-1 presents the utilization obtained for incoming vessels. The reason for the overestimation of the utilizations in S5 and S7 if compared to the model results are the assumptions 1 and 2, and for the underestimation of S6, the route selection.

When looking to the outgoing vessels results (Figure 8-2), given the fact that the same route selection rule is applied, the results are much more similar. Again the overestimation of the hand calculation for S5 and S7 are due to assumptions 1 and 2. In the case of S6, the tidal bounded vessels which are slower than the average used for the approximation balance the overestimation due to faster vessels and the results are almost equivalent.

When adding incoming and outgoing occupations, the results are far below 100%, therefore the hand calculations similarly to the simulation model results indicate that the increasing traffic until 2030

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should not be a problem for the port. However, since neither the traffic nor the spacing regulations are included in this simple hand calculation, nothing can be said about the waiting times experienced by the vessels, not to mention about the performance of the marine operations.

Additionally, such a simplified hand calculation does not allow investigating the contribution of each cause of delay. Still, the tidal window can be closely analyzed. The entire depth restricted section is considered (S6 and S5a).

Figure 8-1 Utilization one-way sections – incoming vessels.

Figure 8-2 Utilization one-way sections – outgoing vessels.

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Figure 8-3 shows a 20 day time series of the tide at the port location. It can be noticed that very short opened tidal windows can occur. Additionally, the tidal window can be closed for almost two days in a row. In order to estimate the utilization of the tidal window in the hand calculations, not only the total time that the window remains open is calculated but the number of vessels that can sail at that particular opening of the window. For instance, vessels take approx. 56 minutes to cross the depth restricted section. Therefore, if the tidal window is opened for 6 hours and taking into account that no incoming vessels are using the channel when outgoing vessels are ready to leave due to the priority, if only one vessel is allowed at the channel at a time 6 vessels can use that window. When allowing for spacing between vessels smaller than the total sailing time (56 minutes), this number is increased. Then, the number of tidal bounded vessels that could use every opening of the tidal window in one year is added and compared to the expected number of tidal bounded calling vessels in that year.

Figure 8-3 Tidal window example of short open period and long closed period.

Figure 8-4 present the results of the utilization of the tidal window when considering only one vessel at the channel at a time, 15 minutes spacing and 30 minutes spacing.

It is important to highlight that the model results here have also a simplification. The total time during which all tidal bounded vessels in a year use the channel is calculated and compared to the total time available for the vessels to sail. Therefore, it does not take into account the actual number of vessels that could use every single opening as it is done for the hand calculations. This explains the difference between model and hand calculation for the 30 minutes spacing.

Again, the same conclusion as for the simulation model is obtained. The tidal window is not a bottleneck for the port. However, nothing can be said about the waiting times or the performance of the marine operations with relation to the tidal window. It is only possible to say, based on Figure 8-5, that there is a chance, although small, of having up to 35 hours of closed tidal windows (water depth below 1.3m+CD).

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Figure 8-4 Utilization of the tidal window – tidal bounded vessels.

Figure 8-5 Histogram of duration of closed tidal window.

Conclusions 8.2

Hand calculations are less time consuming for both gathering input and performing the calculations if compared to simulation models. On the other hand, simulation models can provide outputs with much more details than the hand calculations. It is mostly the complexity of the analysed system and the amount of information required as an output that determines whether using one approach or the other.

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Performing hand calculations can be very useful in giving insight of a system to be studied. However, the conclusions that can be drawn from such a simple estimate are very limited. In complex systems such as this case study, only a simulation model can give a comprehensive understanding of the contribution of every possible cause of delay.

In this case study, both approaches indicate that the marine operations of the Port of Jebel Dhanna/Ruwais should not be congested until 2030. If the conclusion was that the marine operations would be congested, it would be much more difficult to take decisions based on the hand calculations only. For instance, if the utilization of the one-way sections was too high, which would be the most appropriate solution for reducing the marine operations congestion?

Even when the results of the hand calculations indicate no congestion, the use of simulation models is advisable when a full understanding of a complex system is wanted. For instance, in this case study, it is not possible to include in the hand calculations, the influence of outgoing traffic in the route selection of incoming vessels without turning it into more complex computations. The principle of hand calculations is to keep it simple; when serious assumptions and complex calculations are required it might be more appropriated to go for computer simulations.

Once a simulation model is set up, it can be used not only to assess the performance but to evaluate alternatives of port development by means of what-if analysis, making the decision on the best investment much more consistent. Moreover, the visualization provided by computer simulations is an advantage, since it improves the communication between the consultant and the client.

In the following chapter, it is possible to observe additional applications of the implemented model.

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9 HYPOTHETICAL TRAFFIC INCREASE

Since the available traffic forecast, which goes until 2030, is not enough to cause unacceptable congestion due the marine operations and one of the research questions of this graduation work is regarding the bottleneck of the Port of Jebel Dhanna/Ruwais, additional scenarios with artificially increased traffic are simulated. This chapter introduces those additional scenarios and presents the results obtained for the simulations and consequently bottleneck investigation.

Scenario definition 9.1

The occupancy of the terminals in 2030 is analysed in order to support the decision in how to artificially increase the traffic. It is observed in Figure 9-1 that 4 of the 13 destinations have already high occupancies.

Figure 9-1 Berth occupancy in 2030.

The traffic in those destinations is not increased since it would lead to high waiting times due to berth unavailability which is out of the interest of this graduation work. The ChemaWEyaat Construction terminal is temporarily used for receiving construction material for the development of new industries. Therefore, the traffic in this terminal is also not increased. Following the global trend of bigger and bigger vessels, the number of calls at the TAKREER terminal which is dedicated to Coastal Tankers is decreasing. Thus, the traffic destined to this terminal is also unaltered.

The traffic is increased gradually, by applying reduction factors to the 2030 interarrival time of vessels calling at destinations 1 and 6 to 11. See Table 9-1 for the factors definition. Scenario 1 corresponds to previous 2030 scenario. Scenarios 2 to 5 are based on a 10% stepwise reduction of

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the interarrival time and scenarios 6 and 7 were obtained by slightly increasing the traffic until model instabilities are encountered. When further increasing the traffic some of the 20 runs start having stability issues; the number of vessels at the outer anchorage is so high that the system cannot handle the traffic.

Table 9-1 Scenarios definition


1 2 3 4 5 6 7 SimulationTraffic year 2030 2030 2030 2030 2030 2030 2030

Spacing minutes 30 30 30 30 30 30 30 ServiceTimeType - 0 0 0 0 0 0 0

1.Jebel Dhanna– ADCO - 1.00 0.90 0.80 0.70 0.60 0.56 0.52 2.Ruwais Refinery – TAKREER (DW) - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 3.Ruwais Refinery– TAKREER (CT) - 1.00 1.00 1.00 1.00 1.00 1.00 1.00

4.GASCO – Ruwais - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 5.FERTIL – Ruwais - 1.00 1.00 1.00 1.00 1.00 1.00 1.00

6.GASCO – Ruwais (Sulphur) - 1.00 0.90 0.80 0.70 0.60 0.56 0.52 7.BOROUGE (Polyethylene) - 1.00 0.90 0.80 0.70 0.60 0.56 0.52

8.GASCO – Ruwais (Sulphur) 2 - 1.00 0.90 0.80 0.70 0.60 0.56 0.52 9.ChemaWEyaat - Liquids and gas - 1.00 0.90 0.80 0.70 0.60 0.56 0.52 10.ChemaWEyaat - LNG and FRSU - 1.00 0.90 0.80 0.70 0.60 0.56 0.52

11.ChemaWEyaat - Bulk - 1.00 0.90 0.80 0.70 0.60 0.56 0.52 12.ChemaWEyaat - Container - 1.00 1.00 1.00 1.00 1.00 1.00 1.00

13.ChemaWEyaat - Construction - 1.00 1.00 1.00 1.00 1.00 1.00 1.00

The traffic corresponding to each scenario is presented in Table 9-2 and the respective fleet mix can be visualized in Figure 9-2.

Table 9-2 Hypothetical traffic forecast.

YEARS 1 2 3 4 5 6 7 DESTINATIONS CALLS PER YEAR

1 Jebel Dhanna– ADCO 120 133 150 171 200 214 231 2 Ruwais Refinery – TAKREER (DW) 930 930 930 930 930 930 930 3 Ruwais Refinery– TAKREER (CT) 70 70 70 70 70 70 70 4 GASCO – Ruwais 750 750 750 750 750 750 750 5 FERTIL – Ruwais 100 100 100 100 100 100 100 6 GASCO – Ruwais (Sulphur) 70 78 88 100 117 125 135 7 BOROUGE (Polyethylene) 260 289 325 371 433 464 500 8 GASCO – Ruwais (Sulphur) 2 70 78 88 100 117 125 135 9 ChemaWEyaat - Liquids and gas 690 767 863 986 1150 1232 1327

10 ChemaWEyaat - LNG and FRSU 52 58 65 74 87 93 100 11 ChemaWEyaat - Bulk 30 33 38 43 50 54 58 12 ChemaWEyaat - Container 440 440 440 440 440 440 440 13 ChemaWEyaat - Construction 50 50 50 50 50 50 50

TOTAL 3632 3776 3955 4186 4493 4647 4825

The model results are now discussed in the same order as for the previous scenarios analysis. Since the focus is on identifying the bottleneck and the model is implemented in such a way that the lack of pilots or tugs do not delay the vessels, results related to pilots and tugs are not presented. Additionally, since the fleet mix and traffic growth is not real, the corresponding cargo throughput is not calculated.

Figure 9-3 gives a first impression of the waiting times for the simulated scenarios. It can be noticed that the percentage of vessels that wait less than 30 minutes is decreasing and the occurrence of vessels that have to wait more than 19 hours is increasing with increasing traffic.

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Figure 9-2 Fleet mix for the hypothetical traffic increase.

Figure 9-3 Histograms of total and marine operations waiting times – Hypothetical Traffic Increase.

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Percentage of delayed vessels and average waiting times 9.2

For the following results, the same conditions as explained in Chapter 7 apply. Waiting time statistics do not take into account non-delayed vessels and percentages are given w.r.t. vessels that can be affected by that cause of delay.

In the previous simulation, the waiting time due to marine operations was only slightly fluctuating. Now, with increasing traffic, not only the percentage of delayed vessels is increasing reaching up to 91% of the non-exempted vessels but also the waiting time is growing fast (see Table 9-3 and Figure 9-4).

Table 9-3 Percentage of delayed vessels and average total waiting times.

OUTPUT (AVERAGE VALUES) 1 2 3 4 5 6 7 Delayed Vessels - All Causes (%) 83.0% 84.0% 84.5% 86.0% 87.8% 88.3% 89.8%

Total Waiting Time - WT (hours) 9.57 10.26 9.39 10.27 10.17 10.75 14.59 Delayed Vessels - Marine Operations (%) 79.9% 81.6% 82.9% 85.1% 88.0% 88.9% 91.2%

Waiting Time - Marine Operations - WTMO (hours) 3.7 3.8 3.9 4.3 5.1 6.1 10.4

When looking at the percentage of delayed vessels and the waiting times per cause of delay (Table 9-4) it is even clearer that from the 5th scenario onwards the marine operations become the most significant source of delay, see also Figure 9-5.

Starting in scenario 5, the one-way sections of the channel hampers the entrance of vessels to the port increasing the percentage of delayed vessels and the shares of the total waiting time of the causes of delay related to incoming vessels and decreasing the ones related to outgoing vessels (Table 9-4 and Figure 9-6). While the waiting time at the inner anchorage (Figure 9-8) remain almost constant or even decreasing, at the outer anchorage quite a sudden increase in waiting times (from scenario 6 to 7) is noticed considering that the yearly calls have increased by less than 4% only (Figure 9-7).

This sharp increase can be explained by the positive correlation between spacing and traffic waiting times for those scenarios. The correlation plots can be visualized in Figure E-2. The increase in correlation also justifies the reduction in the percentage of delayed vessels for only spacing or only traffic while for both causes together the percentage increases.

Figure 9-4 Total and marine operations only waiting times.

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SCENARIO

OUTPUT (PER CAUSE OF DELAY)

AVERAGE VALUES


ONLY SPACING

ONLY TRAFFIC

TRAFFIC AND

SPACING BERTH ONLY

SPACING ONLY TIDE

TIDE AND

SPACING 1

Delayed Vessels (%)

17.1% 29.1% 20.0% 24.0% 25.7% 11.0% 5.5% 2 17.8% 29.0% 21.8% 23.5% 26.4% 10.7% 5.5% 3 17.6% 29.4% 23.9% 22.2% 27.7% 10.3% 5.4% 4 17.8% 28.8% 27.3% 22.0% 29.1% 9.9% 5.6% 5 17.8% 28.0% 32.8% 20.9% 30.9% 9.3% 5.6% 6 17.4% 27.1% 35.8% 20.6% 32.1% 9.0% 5.5% 7 16.0% 25.6% 42.5% 20.2% 33.1% 8.8% 5.5% 1


0.51 2.14 3.44 21.58 0.66 9.13 7.27 2 0.54 2.25 3.73 24.30 0.66 9.16 7.34 3 0.56 2.38 3.92 22.08 0.68 9.17 7.21 4 0.61 2.63 4.74 24.50 0.69 9.22 7.41 5 0.68 3.03 6.13 22.71 0.70 9.28 7.42 6 0.73 3.23 8.34 21.09 0.71 9.25 7.48 7 0.83 3.53 16.52 20.16 0.73 9.14 7.30

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Figure 9-6 Average waiting time per cause of delay – marine operations only.

Figure 9-7 Waiting time at the outer anchorage per cause of delay.

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Figure 9-8 Waiting time at the inner anchorage per cause of delay.

Number of vessels at the anchorages 9.3

The same logic follows for the number of vessels in the anchorages. While in the outer anchorage an abrupt increase in number of vessels between scenarios occurs, the number of vessels at the inner anchorage is even decreasing (Figure 9-9 and Figure 9-10).

Figure 9-9 Cumulative distribution of vessels in the outer anchorage.

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Figure 9-10 Cumulative distribution of vessels in the inner anchorage.

From this first part of the results analysis it can be concluded that for the simulated fleet mix, when a yearly traffic of approx. 4500 vessels is reached, the one-way sections of the access channels to the Port of Jebel Dhanna/Ruwais start to behave as a bottleneck.

KPIs evaluation 9.4

With increasing traffic, the duration of the marine operations is getting closer to the duration of the quay operations (Figure 9-11). This fact by itself can give an indication of low performance. It is very likely that customers do not appreciate to take longer to access the port than to have their cargo loaded.


Output (Average values) 1 2 3 4 5 6 7 Total Turnaround Time - TR (hours) 52.6 53.1 52.0 52.6 52.3 52.7 56.1

Turnaround Time (Marine Operations) - TRMO

(hours) 16.4 16.6 16.7 17.0 17.7 18.7 22.5

Service Time - st (hours) 28.4 28.2 27.8 27.6 27.2 27.1 26.9 Waiting Time /Turnaround Time (Marine Operations) - KPI1𝑀𝑂

% 17.1% 17.5% 18.0% 19.4% 21.6% 23.4% 27.7%

Regarding the rate of waiting, the limit of 30% was previously stablished as acceptable. However, in scenario 5, the waiting time due to the marine operations reaches 21.6% of the corresponding turnaround time (Table 9-5 and Figure 9-12) and the situation can already be regarded as undesirable. Therefore, for this given case study, it might be that 20% is a better average value to be regarded as a limit.

In Figure 9-11 it can be observed that the service time is slightly decreasing. This fact occurs due to the change in the fleet mix. However, Table 9-6 shows that the rate of waiting time due to berth unavailability with respect to the service time is decreasing. In scenario 7 the situation is so extreme that a shift between waiting reasons occur. Instead of the vessel to be in the inner anchorage waiting

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for the berth to become available she is in the outer anchorage waiting for the one-way sections to be cleared.

Figure 9-11 Turnaround time and service time.

Figure 9-12 Waiting time over turnaround time (marine operations).

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Table 9-6 shows the average KPIs per cause of delay, 10, 50 and 90% percentiles can be seen in Figure 9-13.


SCENARIO

OUTPUT (PER CAUSE OF

DELAY)


ONLY SPACING

ONLY TRAFFIC

TRAFFIC AND

SPACING BERTH ONLY

SPACING ONLY TIDE

TIDE AND SPACING

1 Waiting Time /

Turnaround Time (Marine Operations only) - 𝐊𝐏𝐈𝟏𝐌𝐎 per

cause

3.4% 11.2% 17.6% - 4.1% 33.3% 28.7% 2 3.6% 11.8% 18.4% - 4.1% 33.2% 29.0% 3 3.7% 12.3% 19.2% - 4.1% 33.0% 28.5% 4 4.0% 13.3% 21.5% - 4.2% 32.8% 28.4% 5 4.4% 14.9% 25.1% - 4.1% 32.1% 27.8% 6 4.7% 15.7% 28.2% - 4.0% 31.4% 27.5% 7 5.2% 16.7% 34.9% - 3.9% 29.8% 25.8% 1

Waiting Time / Service Time

KPI2

- - - 97.2% - - - 2 - - - 110.4% - - - 3 - - - 99.6% - - - 4 - - - 113.4% - - - 5 - - - 104.6% - - - 6 - - - 94.4% - - - 7 - - - 91.1% - - -

Figure 9-13 Waiting time over turnaround time per cause of delay (marine operations).

Conclusions 9.5

By increasing the traffic step by step it is possible to identify when a shift in the main cause of delay occurs. For the simulated fleet mix, the marine operations causes of delay start having the same weight as the berth unavailability cause from scenario 5 onwards (approx. 4500 vessels per year).

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A waiting rate (marine operations waiting time over marine operations turnaround time) of approx. 20% corresponds to the situation at which the congestion due to marine operations is regarded as unacceptable. Therefore, for this case study, the acceptable limit for this KPI is no longer taken as 30% but as 20%.

The causes of delay related to incoming vessels are the most affected ones (traffic and spacing regulations in the one-way sections). It is important to highlight that this situation is very sensitive to the fleet mix. If the number of tidal bounded vessels and by consequence also the utilization of the tidal window had greatly increased, it could be that the delays due to the tidal window were substantial. However, when the situation is as extreme as it is the scenario 7, all causes of delay that affect outgoing vessels lose their importance since the capacity of vessels to enter the port is reduced.

It can be concluded that when a yearly traffic of approx. 4500 vessels is expected, and if the distribution of vessel classes is similar to the simulated fleet mix, the one-way sections of the access channels to the Port of Jebel Dhanna/Ruwais will experience a very low performance. When 4800 vessels per year are expected, the existing marine infrastructure and port regulations are unable to cope with the demand.

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10 MEASURES TO REDUCE CONGESTION

Now that an unacceptable performance of the marine operations is identified, and the traffic for which congestion has reached an intolerable level is known, alternatives that might improve the performance and possibly allow the port to cope with the same traffic demand are tested.

Since the bottleneck is at the one-way sections of the channels, interventions in these sections are proposed. By increasing the percentage of vessels using the alternative routes to enter the port instead of the main access channel, the number of vessels subject to the bottleneck is reduced and consequently the congestion. The other alternatives require physical changes of the bottleneck itself, such as deepening and widening.

Two control variables are introduced to simulate these alternatives, the PilotageExemption Scenario and the DW_Scenario. The first alternative proposes that more vessels enter the port via the Relief Route. The actual Port Regulations state that only vessels with a pilotage exemption certificate are allowed to use this route (PilotageExemption Scenario=0). The Port Regulations also restrict the size of vessels that can apply for a pilotage exemption certificate. For the given fleet, only class BV01 fulfills this criterion.

However, when considering the water depth at MLLW across the Relief Route and the UKC requirements, two other classes of vessels would be able use this route either in ballast or laden conditions. Therefore, the first of the proposed measures (PilotageExemption Scenario=1) is to allocate those three vessel classes without depth restrictions irrespective of the tidal level to the Relief Route (instead of only one).

The drawback of this alternative is that those additional classes do not fulfill the criterion to apply for a pilotage exemption certificate. Therefore, in order to make this alternative possible to be implemented, either the criterion for exempted vessels should be changed or the Pilot boarding would have to happen in a different place than at the Ghasha Pilot Station. In this chapter the number of pilots and tugs is not evaluated; therefore, there is no practical difference between both required changes.

The second alternative to be tested is to change the route selection criteria for incoming vessels and to “deepen” the Stewart Channel. The current situation defines that incoming vessels with suitable draught to sail through either the Deep Water or the Stewart Channels will use whichever route is free (without encounters) but preferably the Stewart Channel (the shallowest). For outgoing vessels the current regulation specifies that vessels that can use the Stewart Channel will do so (DW_Scenario=0). The proposed alternative is that all outgoing vessels irrespective to the draught are leaving the port through the Deep Water Channel and all incoming vessels are entering the port through the Stewart Channel (DW_Scenario=1). In order to make that possible, the Stewart Channel has to be dredged since three vessel classes have to use the Deep Water Channel at current conditions. Only making the Stewart Channel deeper and routing all the incoming vessels to this channel reduces even more the capacity of the access channels since incoming vessels would have only one option to enter the port instead of two. Therefore, this intervention is only simulated in combination with others.

A third alternative is to turn section S5 into a two-way channel, where encounters are allowed but overtaking is not (DW_Scenario=2). Additionally, a combination of both measures is analyzed. Incoming vessels enter the port via the Stewart Channel and outgoing vessel leave through the Deep Water Channel. Incoming and outgoing vessels can meet while sailing through S5. This combination corresponds to the control variable DW_Scenario equal to 3.

The PilotageExemption Scenario (from 0 to 1) and the DW_Scenario (from 0 to 3) variables can be combined originating 8 measures which are described in the following section.

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Some of the proposed alternatives require capital investments. The reduction in congestion achieved by each measure can then be used by the port planner to evaluate the alternatives and identify the one with the best cost/benefit ratio. For this specific case study, the number of served vessels and the cargo throughput are the same for all alternatives. Therefore, the benefits given by accrued tariffs are the same in all situations; however, the performance of the marine operations varies significantly with each simulated measure.

Measures description 10.1

As stated in the previous section, eight measures can be derived by the combination of the two additional control variables. The traffic for all measures is the same as for scenario 7 of previous chapter, approx. 4800 vessels a year. This selection is made since it is the most critical simulated traffic, when the one-way sections of the channel represent a very well defined bottleneck to the port. Measure 0 is equivalent to the previous scenario 7 and corresponds to both control variables are equal to 0. This scenario is here replicated in order to facilitate the comparison between alternatives.

The combination of PilotageExemption Scenario equal to 0 and DW_Scenario equal to 1 has resulted in an even more congested situation. The reason for that is the reduction of route options to be followed by incoming vessels. When routing all incoming vessel through the Stewart Channel without turning S5 into a two-way section, the capacity of vessels to enter the port is reduced since only one channel can be used instead of two. Therefore, this combination is not simulated since it cannot cope with the same traffic as the other ones.

Table 10-1 presents a short description of each measure. Measure 1 is the only alternative with no costs involved.

Table 10-1 Measures description.

MEASURE DESCRIPTION RELATED COSTS 0 Scenario 7 (vide paragraph 9.1) - 1 Measure 0 + Relief Route: BV01, BV02 and BV03 -

2 Incoming vessels via Stewart Channel, outgoing vessels via Deep Water Channel + Relief Route: BV01, BV02 and BV03 Deepening the Stewart Channel

3 Measure 0 + S5 is two-way operated Widening S5 4 Measure 3 + Relief Route: BV01, BV02 and BV03 Widening S5

5 Incoming vessels via Stewart Channel, outgoing vessels via Deep Water Channel + S5 is two-way operated

Deepening the Stewart Channel and widening S5

6 Measure 5 + Relief Route: BV01, BV02 and BV03 Deepening the Stewart Channel and widening S5

The percentage of exempted vessels when PilotageExemption Scenario control variable is equal to zero is 3.5% and when equal to one is 16%. It is important to highlight that exempted vessels are not subject to any marine operation source of delay. They can only be delayed by berth unavailability reasons therefore they are not taken into account in marine operations percentage results.

The final set of control variables of the measures to be simulated is presented in Table 10-2. The control variable values are described in both this and the previous chapter.

The described alternatives were implemented and verified. The results of the simulations are now presented. Figure 10-1 gives the first impression of the total and marine operations waiting times. For all simulated measures it is possible to notice a significant increase in occurrence of waiting times smaller than 30 minutes and a reduction of waiting times higher than 19 hours.

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Table 10-2 Measures definition

CONTROL VARIABLE MEASURE

0 1 2 3 4 5 6 SimulationTraffic year 2030 2030 2030 2030 2030 2030 2030

Spacing minutes 30 30 30 30 30 30 30 ServiceTimeType - 0 0 0 0 0 0 0

PilotageExemption Scenario - 0 1 1 0 1 0 1 DW_Scenario - 0 0 1 2 2 3 3

1.Jebel Dhanna– ADCO - 0.52 0.52 0.52 0.52 0.52 0.52 0.52 2.Ruwais Refinery – TAKREER (DW) - 1 1 1 1 1 1 1 3.Ruwais Refinery– TAKREER (CT) - 1 1 1 1 1 1 1

4.GASCO – Ruwais - 1 1 1 1 1 1 1 5.FERTIL – Ruwais - 1 1 1 1 1 1 1

6.GASCO – Ruwais (Sulphur) - 0.52 0.52 0.52 0.52 0.52 0.52 0.52 7.BOROUGE (Polyethylene) - 0.52 0.52 0.52 0.52 0.52 0.52 0.52

8.GASCO – Ruwais (Sulphur) 2 - 0.52 0.52 0.52 0.52 0.52 0.52 0.52 9.ChemaWEyaat - Liquids and gas - 0.52 0.52 0.52 0.52 0.52 0.52 0.52 10.ChemaWEyaat - LNG and FRSU - 0.52 0.52 0.52 0.52 0.52 0.52 0.52

11.ChemaWEyaat - Bulk - 0.52 0.52 0.52 0.52 0.52 0.52 0.52 12.ChemaWEyaat - Container - 1 1 1 1 1 1 1

13.ChemaWEyaat - Construction - 1 1 1 1 1 1 1

Figure 10-1 Histograms of total and marine operations waiting times – Measures to Reduce Congestion.

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Percentage of delayed vessels and average waiting times 10.2

For the following results, the same conditions as explained in Chapter 7 apply. Waiting time statistics do not take into account non-delayed vessels and percentages are given w.r.t. vessels that can be affected by that cause of delay.

Table 10-3 shows the resulting percentages of delayed vessels and average waiting times. A significant reduction in all outputs is noticed. The reduction in waiting times both for all causes and marine operations only can be observed in Figure 10-2.

Table 10-3 Percentage of delayed vessels and average total waiting times.

OUTPUT (AVERAGE VALUES) 0 1 2 3 4 5 6 Delayed Vessels - All Causes (%) 89.8% 75.0% 73.4% 78.2% 65.9% 67.9% 57.5%

Total Waiting Time - WT (hours) 14.6 9.4 8.7 8.3 9.6 7.8 9.4 Delayed Vessels - Marine Operations (%) 91.2% 85.1% 82.8% 76.2% 71.0% 63.0% 58.6%

Waiting Time - Marine Operations - WTMO (hours) 10.4 4.4 3.6 3.4 3.6 2.5 2.9

Figure 10-2 All causes and marine operation related waiting times.

In Figure 10-3 it is can be observed that measure 1, which has no costs involved, reaches the same pattern of waiting time proportions as for scenario 4 of previous chapter (see Figure 9-5) . The same average waiting time as scenario 3 from previous chapter is achieved. This means that if measure 1 is implemented, 4800 vessels can be handled instead of approx. 4000 with the same performance.

Measures 2, 3 and 4 present an advantage compared to measure 1 especially due to reductions in the percentage of delayed vessels and in the share corresponding to the traffic cause of delay. However, since these alternatives involve costs either for making S5 wider or for deepening S7, the benefit of these reductions has to be compared to the expenses of implementing the alternatives.

The configuration of measures 5 and 6 allow for the entire network of access channels to be two-way operated. Therefore, there is no need for incoming vessel to wait for outgoing vessels anymore; the traffic share of the average waiting time causes of delay vanishes.

In Table 10-4 it is possible to notice that the percentage of delayed vessels and the waiting time of the traffic and spacing regulations (which affect the incoming vessels) are significantly reduced for all simulated measures. Outgoing vessels and vessels waiting for the berth are hardly affected. The reduction in waiting times can be visualized in Figure 10-4 and Figure 10-5.

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Figure 10-6 shows a progressive reduction of the traffic cause of delay. From measure 4, the tidal window cause of delay is more significant than the sum of all other causes.

Regarding the correlation between spacing and traffic waiting times for incoming vessels, the positive correlation observed for the last scenarios of the previous chapter is reduced (see Figure E-3). For the outgoing vessels, the same pattern as for all other simulations is obtained, no correlation can be observed.


MEASURE

OUTPUT (PER CAUSE OF DELAY)

AVERAGE VALUES


ONLY SPACING

ONLY TRAFFIC

TRAFFIC AND

SPACING BERTH ONLY

SPACING ONLY TIDE

TIDE AND

SPACING 0

Delayed Vessels (%)

16.0% 25.6% 42.5% 20.2% 33.1% 8.8% 5.5% 1 17.8% 28.3% 27.8% 21.9% 28.7% 10.2% 6.2% 2 18.7% 30.0% 20.6% 21.6% 28.1% 10.3% 6.3% 3 24.9% 15.0% 17.3% 19.9% 32.7% 8.9% 5.4% 4 21.7% 13.9% 14.6% 22.0% 28.2% 10.2% 6.3% 5 30.7% - - 19.7% 32.4% 8.8% 5.6% 6 26.1% - - 22.0% 28.0% 10.2% 6.2% 0


0.83 3.53 16.52 20.16 0.73 9.14 7.30 1 0.62 2.71 4.65 20.33 0.71 9.22 7.50 2 0.53 1.95 3.15 20.67 0.68 9.35 7.67 3 0.55 2.40 3.74 20.81 0.71 9.24 7.36 4 0.50 2.45 3.68 21.68 0.69 9.25 7.39 5 0.36 - - 19.65 0.68 9.62 7.67 6 0.34 - - 20.66 0.68 9.46 7.49

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Figure 10-4 Waiting time at the outer anchorage.

Figure 10-5 Waiting time at the inner anchorage.

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Figure 10-6 Average waiting time per cause of delay- marine operations.

Number of vessels at the anchorages 10.3

Regarding the number of vessels at the anchorages, Figure 10-7 clearly shows that the inner anchorage is almost not affected.

Figure 10-7 Number of vessels at the Inner Anchorage.

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The number of vessels at the outer anchorage (Figure 10-8) is reduced for all alternatives compared to measure 0.

Figure 10-8 Number of vessels at the Outer Anchorage.

KPIs evaluation 10.4

The total turnaround time, the marine operations turnaround time and the service time are presented in Figure 10-9 and Table 10-5. An expressive reduction in the marine operations turnaround time can be noticed for all alternatives. The waiting time due to marine operation over the marine operations turnaround time is presented both in Table 10-5 and Figure 10-10. As a reminder, the tables present average values and the 10%, 50% and 90% percentiles can be visualized in the box plots presented in figures.


Output (Average values) 0 1 2 3 4 5 6 Total Turnaround Time - TR (hours) 56.1 49.8 49.1 49.6 49.1 48.4 48.1

Turnaround Time (Marine Operations) - TRMO (hours) 22.5 16.2 15.5 15.9 15.2 14.9 14.4

Service Time - st (hours) 26.9 27.0 27.0 27.0 27.0 27.0 26.9 Waiting Time /Turnaround Time (Marine Operations) - KPI1𝑀𝑂 % 27.7% 19.7% 16.7% 15.7% 16.3% 11.5% 12.8%

The average waiting rate is for some alternatives reduced to even lower levels than the current situation (15.4% in 2014). The acceptance of the marine operation performance in measure 1, which is the only one without requiring expenses, is considered just satisfactory. However, operating that close to the limit is not advisable. Therefore, this measure is efficient in reducing congestion from an extremely unwanted situation to an acceptable one but is not sufficient to bring the performance to a safe operation level.

The increase in the waiting time and consequently in the waiting rate from measure 3 to 4 and from measure 5 to 6 has to do with the decrease in the percentage of delayed vessels. Fewer vessels are delayed in measures 4 and 6 however they wait slightly more than the delayed vessels in measures 3 and 5.

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Measures 2 to 6 are all within the acceptable limit and the selection of the most promising measure is done at the feasibility study stage based on all results presented in this section and the respective calculated benefits and costs.

Figure 10-9 Turnaround time comparisons.

Figure 10-10 Waiting time over turnaround time (marine operations).

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Table 10-6 and Figure 10-11 shows the waiting rates separated per cause of delay. A decrease in the marine operations waiting rates for incoming vessels is observed while for outgoing vessels an increase occurs. This can be explained since the simulated measures allow for more vessels to enter the port than in measure 0 taking the outgoing vessels delays to the same levels as previously. In measure 0 these causes of delay had their influence reduced since the entrance to the port was hampered by the one-way section.


MEASURE

OUTPUT (PER CAUSE OF

DELAY)


ONLY SPACING

ONLY TRAFFIC

TRAFFIC AND

SPACING BERTH ONLY

SPACING ONLY TIDE

TIDE AND SPACING

0 Waiting Time /

Turnaround Time (Marine Operations only) - 𝐊𝐏𝐈𝟏𝐌𝐎 per

cause

5.2% 16.7% 34.9% - 3.9% 29.8% 25.8% 1 4.0% 13.4% 21.4% - 4.2% 32.7% 28.8% 2 3.5% 10.8% 16.2% - 4.1% 34.4% 30.4% 3 3.6% 11.7% 18.9% - 4.5% 33.7% 29.2% 4 3.3% 11.6% 18.3% - 4.4% 33.9% 29.5% 5 2.5% - - - 4.5% 36.8% 31.8% 6 2.4% - - - 4.5% 36.4% 31.4% 0

Waiting Time / Service Time

KPI2

- - - 91.1% - - - 1 - - - 88.0% - - - 2 - - - 90.8% - - - 3 - - - 94.9% - - - 4 - - - 93.6% - - - 5 - - - 87.8% - - - 6 - - - 89.5% - - -

Figure 10-11 Waiting time over turnaround time per cause of delay (marine operations).

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Conclusions 10.5

It can be concluded that all simulated measures to reduce congestions are effective in doing so. The only alternative without any related costs (measure 1) brings the waiting times back to the acceptable limit (below 20%); however, operating that close to the limit is not advisable. Therefore, only increasing the number of vessels classes that are able to use the Relief Route from one to three is not sufficient to bring the performance to a safe operation level when 4800 vessels per year are expected. Still, this measure is efficient in reducing congestion from an extremely unwanted situation to a more acceptable one.

The most efficient measure is the one simulated in measure 6 but this is also the most costly one. The combination of widening S5, deepening the Stewart Channel, and routing three vessel classes instead of one through the Relief Route reduces the percentage of delayed vessels from 91% to 59% and the average waiting time due to marine operations from 10.4 to 2.9 hours when compared to measure 0. The waiting rate of measure 6 is approx. 13% which is even lower than the 15% obtained for the 2014 traffic and port configuration (see paragraph 7.1.3), which means that, even with 2900 additional calls per year than in 2014 a better performance is obtained. Evidently, the fact that in measure 6 the additional terminal is included should not be disregarded. When comparing measure 6 to the original 2030 situation (see paragraph 7.1.3), 1200 additional calls per year can be handled with an even better performance.

All simulated measure improve the marine operations performance for the given traffic and fleet mix. However, each measure has a different cost related to it. Therefore, even though measure 6 is the most effective in reducing the marine operations congestion, only a cost benefit analysis can indicate the best alternative.

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11 EVALUATION OF THE USE OF FLEXSIM

The applicability of the FlexSim tool to simulate marine operations is evaluated in this chapter. This chapter contains personal opinion and the reader should be aware that the level of experience of the modeller is an important factor which may influence the evaluation. The six criteria to be evaluated are (see paragraph 1.5):

FlexSim should be able to reproduce all marine operations. (Adequacy) Adding new functionalities, other than the default ones, should not be significantly

time/effort demanding. (Adaptability) Changes in the model, for instance when simulating various alternatives, should be easily

implemented. (Flexibility) The program should not crash frequently while programming or simulating. (Stability) The simulation time should not be a limiting factor. The time that it takes for the model to

run should be adequate to performing feasibility studies. (Simulation time) The output should be easily obtained from the model. Client-friendly results directly attained

by the model, without too much post processing, are appreciated. (Quality of model output)

The scale of the evaluation is presented in Table 11-1 and the evaluation results in Table 11-2.

Table 11-1 Evaluation scale.

o - - - - + + ++ Neutral Very poor Poor Fair Good Very good

Table 11-2 Evaluation results.

# CRITERIA EVALUATION 1 Adequacy + 2 Adaptability + 3 Flexibility + 4 Stability o 5 Simulation time ++ 6 Quality of model ouptut -

A brief discussion about the evaluation of every criterion is now given.

1) All required functionalities were possible to be implemented in FlexSim. However, a lot of customization and programming was needed since the traffic control default functions of FlexSim are very simple, and none of them could have been used in this simulation model.

2) The priority for outgoing vessels; the tidal window; the prevention of encounters and the spacing regulation, required timetables prediction. Since these timetables are used by all just mentioned regulations, a very complicated iterative process was needed in order to ensure their functioning. Therefore, some of the functionalities demanded significant time for being implemented; and although the time for implementing new functions is very much dependent on the experience of the modeller, it would be appreciated if more default functions of FlexSim could have been used in order to reduce programming time.

3) Some alternatives are more difficult to implement than others, but it is likely that once the model is set up, the implementation of alternatives is less time demanding than the first model version.

4) Stability problems were experienced due to lack of experience with the simulation language and tool at the beginning of the model implementation. However, the debugging tools are extremely helpful in solving the issues and acted as learning tools which assisted on improving the modeller skills on programing.

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5) The simulation time is closely related to the yearly traffic to be simulated. For instance, when simulating the highest traffic for 7 scenarios and 20 runs each, the simulation time is in the order of 30 minutes which is very much acceptable. For less intense traffic and 1 scenario with 20 runs, the simulation time is in the order of 2 minutes.

6) The standard FlexSim output graphs are useful for quickly visualizing what is happening during and after the simulations. However, in order to be able to present them in a reporting level some extra effort is required. Therefore, it was opted to use the model output tables for post processing and generating graphs. It might be that with more experience in the FlexSim tool the available reports, graphs and charts can be better explored.

The FlexSim simulation software is considered to be adequate to perform port’s marine operations simulations. Realistic results are obtained with more than acceptable simulation times and with a moderate time for implementation, which decreases with the modeller increasing experience. However, the possibility of using default FlexSim functions for implementing the traffic regulations would be appreciated.

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12 CONCLUSIONS AND RECOMMENDATIONS

Assessing the current and future performance of The Port of Jebel Dhanna/Ruwais marine operations by using a simulation model in order to evaluate the congestion development and support decision making is the objective of this MSc Thesis.

During the accomplishment of this objective, conclusions were drawn, and answers to the research questions were encountered (see paragraph 1.4). This chapter gathers all these conclusions and answers (section 12.1) and presents recommendations for further research (section 12.2).

Conclusions 12.1

Several research questions were identified as fundamental for correctly achieving the main objective of this graduation work. The conclusions obtained during the elaboration of this thesis are answers to these research questions; therefore, the questions are repeated in this section, and the answers are given immediately after each one of them. The two first questions complement each other and therefore are answered together.

Which processes are necessary to be represented in detail to assess the Port of Jebel Dhanna/Ruwais marine operations performance in the context of a feasibility study? What is the level of detail required for the input parameters to represent the Port of Jebel Dhanna/Ruwais real situation?

The navigational service system processes implemented in the model are: vessels arrival; verifications for clearance and route assignment; vessels sailing and quay operations. The vessels arrival is defined based on intearrival time distributions given for each terminal of call; the vessel sailing process does not take into account acceleration and deceleration rates; the tidal window is verified with a resolution of ten minutes; and the quay operations are defined as a time expended at berth for loading operations (service time) plus paperwork.

Combined with the definition of processes that have to be modelled in detail, the level of detail of the input parameter is also discussed in this report. The identified required inputs are: traffic forecast; port operation; weather conditions; tidal window; vessels’ characteristics; port layout and traffic rules. Section 3.8 defines the choices made in the level of detail of each of these inputs for the implementation of this case study model; whenever possible, different situations than this case study are introduced and discussed. Additionally, during the assessment of the performance it is concluded that the fleet mix has a large influence in the model results; therefore, special attention should be given to this input.

The level of detail required for the model to correctly represent each of these processes was defined based on common sense, always taking into account the feasibility study planning stage; however, some variables, for which the influence on the marine operation performance is not directly known, require further investigation in order to support the decision on the simplifications to be taken. For instance, there was not enough evidence to support the decision on the level of detail of both the spacing regulation and the service time. Therefore, a sensitivity analysis (section 5.3) is performed in order to identify the level of detail required for representing these processes. For the spacing regulation, three values for spacing are tested: zero; fifteen; and thirty minutes. For the service time, four options are tested: deterministic value based on total cargo and berth productivity; same as the previous calculation, however with the productivity varying uniformly between 80-120%; an Erlang-k distributed service time; and a deterministic value equal to two hours and 10 minutes which is the minimum required to ensure the priority for outgoing vessels plus a margin (see section 5.2).

It can be concluded based on the sensitivity analysis that the spacing regulation has to be well defined in order to avoid an overestimation of the access channel capacity. Regarding the

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service time, it can be concluded that this variable has a large effect on the total turnaround and waiting times; however, it does not affect results which are related exclusively to the marine operations. Nevertheless, it does affect the estimation of required number of tugs and pilots. Therefore, when the estimation of number of pilots and tugs is not a requirement of the model, the use of a fixed and short duration of service time could be applied given that it reduces significantly the amount of input information and decreases the level of detail of the quay operations process.

Based on the success in achieving the thesis objectives and in validating the model (see remarks in paragraph 6.3), it can be concluded that the selected level of detail of input variables is adequate for performing an assessment of the marine operations of the Port of Jebel Dhanna/Ruwais in a feasibility study context.

Which are the Key Performance Indicators for Port Marine Operations that have to be assessed for this case study accomplishment?

In section 3.9 three KPIs are identified as essential for evaluating the marine operations performance at planning stages: the waiting time / turnaround time; the waiting time / service time (average rate of waiting); and the berth occupancy.

During the sensitivity analysis a lack of influence of the service time on the marine operations is identified. Consequently, the total turnaround time, which has its main contribution given by the service time at berth, is not of significance for the evaluation of the marine operations. Therefore, the main KPI for assessing the performance of the marine operations of a port is identified as being the waiting time due to marine operations over the marine operations turnaround time.

However, comparisons between the marine operations turnaround time and the total turnaround time or the service time are valuable in providing an overall understanding of the port functioning. In this case study simulation model, not enough information was available in order to calibrate the service time. Therefore, the total turnaround time, the total waiting time, the waiting time due to berth unavailability over the service time and the berth occupancy, which are presented to assist the interpretation of differences between scenarios, should be interpreted without overlooking the underlying caveats.

Focus should be given to the marine operations causes of delay and to the waiting time over marine operations turnaround time as indicators of the Port of Jebel Dhanna / Ruwais marine operations performance.

Additionally, a conclusion regarding the acceptable limit for this KPI is drawn from Chapter 9. A waiting rate (KPI1𝑀𝑂) over 20% corresponds to the situation at which the congestion due to marine operations is regarded as unacceptable. Therefore, 20% is considered to be the acceptability limit for this case study; it is advised to operate at lower waiting rates than that.

How is the performance of the Port of Jebel Dhanna/Ruwais marine operations for the actual and forecasted traffic and which are the bottlenecks?

The waiting rates for 2016, 2022 and 2030 are comparable to the waiting rate of 2014, and are all smaller than 20%. Given the fact that in 2014 the Port of Jebel Dhanna/Ruwais is considered to be operating in a good performance, the performance of years 2016, 2022, and 2030 are also considered acceptable. No real bottleneck is identified from 2014 to 2030. However, since the results are very sensitive to the fleet mix, more information and monitoring of changes with respect to this input is advisable.

In order to investigate which would be the bottleneck with an increasing traffic (higher than the one expected for 2030), hypothetical scenarios with increasing number of calls per year were simulated. According to the results (presented in Chapter 9), it can be concluded that when a yearly traffic of approx. 4500 vessels is expected, and if the distribution of vessel classes is similar to the

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simulated fleet mix, the marine operations of the Port of Jebel Dhanna/Ruwais will experience a very low performance. The bottleneck of the Port of Jebel Dhanna/Ruwais, for the simulated traffic and fleet mix, is at the one-way sections of the access channel. When the traffic is increased even more, up to approx. 4800 vessels a year, the port cannot handle the forecasted traffic and the outer anchorage is not emptied anymore within the one year simulation period. This situation occurs due to the prioritization of outgoing vessels.

Which are the most effective measures to solve the bottlenecks and improve the Port of Jebel Dhanna/Ruwais performance?

Three interventions are proposed: deepening the Stewart Channel and using it for incoming vessels only; widening one-way section shared between the Stewart and the Deep Water Channels; and the routing of three vessel classes instead of only one through the Relief Route. Eight measures are derived from the combination of those three interventions. However, only making the Stewart Channel deeper and routing all the incoming vessels to this channel reduces even more the capacity of the access channels since incoming vessels would have only one option to enter the port instead of two. Therefore, this intervention alone does not improve the performance and is simulated only I combination with the other ones.

All other combinations of interventions are simulated; and all of them result in improvement of the marine operations performance for the given traffic and fleet mix. However, each alternative has a different cost related to it. Therefore, even though the combination of the three interventions is the measure that presents the greatest reduction in waiting times due to marine operations if compared to the no-action measure, only a cost benefit analysis can indicate the best alternative.

Which is the added value of performing simulations in order to assess marine operations performance compared to hand calculations?

In this case study, both approaches indicate that the marine operations of the Port of Jebel Dhanna/Ruwais should not be congested until 2030. If the conclusion was that the marine operations would be congested, it would be much more difficult to take decisions based on the hand calculations only. Therefore, it can be concluded that performing hand calculations can be very useful in giving insight of a system to be studied; however, in complex systems such as this case study, only a simulation model can give a comprehensive understanding of the contribution of every possible cause of delay.

Even when the results of the hand calculations indicate no congestion, the use of simulation models is advisable when a full understanding of a complex system is wanted. For instance, in this case study, it is not possible to include in the hand calculations, the influence of outgoing traffic in the route selection of incoming vessels without turning it into more complex computations. The principle of hand calculations is to keep it simple; when serious assumptions and complex calculations are required it might be more appropriated to go for computer simulations.

Additionally, once a simulation model is set up, it can be used not only to assess the performance but to evaluate alternatives of port development by means of what-if analysis, making the decision on the best investment much more consistent. In many cases, it is not possible to test different alternatives by performing hand calculations without turning them into more complicated computations, which is not desirable.

Moreover, the visualization provided by computer simulations is an advantage since it improves the communication between the consultant and the client.

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Is the FlexSim software an appropriate tool for conducting marine operations performance assessments?

The tasks to be executed based on the model results are: to assess the ability of the port to handle the forecasted traffic and throughput; to investigate the development of congestion with increasing traffic; to assess the performance of the port with increasing traffic; to identify bottlenecks (sources of delay); and to estimate the number of pilots and tugs needed to provide the services. All the listed tasks were properly executed supported by the simulation model outputs.

Based on that, and on the evaluation of the use of this simulation software, FlexSim is considered to be adequate to perform port’s marine operations simulations. Realistic results are obtained with more than acceptable simulation times and with a moderate time for implementation, which decreases with the modeller increasing experience. However, the possibility of using default FlexSim functions for implementing the traffic regulations would be appreciated.

Recommendations 12.2

The recommendations for the case study continuation and also for further research on assessment of marine operations performance by means of simulation are given in this section.

Case study continuation

The focus of this MSc Thesis is on the marine operations, but once the model is set up, the effort of extending the model and including the cargo handling services in more detail can be valuable, especially given that the greatest contribution to the average waiting time is the berth unavailability. The model could be then used to optimize berth occupancies and reduce the waiting times due to berth unavailability. If the port planner considers appropriate to extend the model, the model outputs should also be reviewed, since results per terminal of call are important in that case.

In case the model is extended, data should be gathered to calibrate the service time at berth and the validation can then be repeated in order to check if the increase in details at the quay side leads to smaller differences between simulated and AIS data.

The priority for outgoing vessels is a Port Regulation that affects largely the incoming vessels waiting times. The removal of this regulation and inclusion of a rule dictating the reversion of the one-way sections could be tested. Additionally, the intervals between reversion in terms of time or number of vessels in each direction could be tested, and this regulation optimized.

Further research on marine operations performance

Regarding further research on assessment of marine operations performance, it is recommended to verify, for both congested and non-congested ports, which is the actual waiting time due to marine operations over marine operations turnaround time rates in order to better establish this KPI acceptable value. Applying questionnaires to both customers and ports is also an alternative for identifying acceptable limits. It is known that discussions on benchmarking issues are recurrent; therefore any effort in establishing guidelines or even merely specific standard values is appreciated.

Regarding the level of detail of processes and related input parameters, there is a lot to be studied on this subject. For instance, one of the model assumptions is that no acceleration and deceleration of vessels is considered. This assumption is taken given the large scale of the case study access channels network. It might be that for shorter channels this assumption is not valid anymore. Defining the boundaries for changing from a higher to a lower aggregation level for each of the processes and related input parameters is a very difficult task to be performed; however, this kind of information is relevant and could significantly facilitate the implementation of simulation models to be used for the same purpose.

128


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Almaz, O. A., & Altiok, T. (2012). Simulation modelling of the vessel traffic in Delaware River: Impact of deepening on port performance. Simulation Modelling Practice and Theory, 22, 146-165. doi: 10.1016/j.simpat.2011.12.004

Altiok, T., Almaz, O. A., & Ghafoori, A. (2012). Modeling and Analysis of the Vessel Traffic in the Delaware River and Bay Area Risk Assessment and Mitigation. Piscataway, NJ, USA: Rutgers - Center for Advanced Infrastructure and Transportation, Laboratory for Port Security (CAIT-LPS).

Arango, C., Cortés, P., Muñuzuri, J., & Onieva, L. (2011). Berth allocation planning in Seville inland port by simulation and optimisation. Advanced Engineering Informatics, 25(3), 452-461. doi: 10.1016/j.aei.2011.05.001

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Francou, B. (2010). Diploma in Port Management - Module 3 - Commercial Management. London: IBC Global Academy.

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MarineTraffic. (2014). Retrieved 08/04/2014 18:07 CEST https://www.marinetraffic.com

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Siregar, P. J. B. (1995). A Study on the Concept Design Rules for Approach Channels. (MSc Thesis), Delft University of Technology.

Sonnessa, M. (2004). Modelling and Simulation of Complex Systems (PhD Thesis), University of Torino, Italy.

Thiers, G. F., & Janssens, G. K. (1998). A Port Simulation Model as a Permanent Decision Instrument. Simulation, 71(2), 117-125

UNCTAD. (1985). Port Development - A Handbook for Planners in Developing Countries (Second ed.). New York: United Nations Conference on Trade and Development.

van de Ruit, G. J., van Schuylenberg, M., & Ottjes, J. A. (1995). Simulation of Shipping Traffic Flow in The Maasvlakte Port Area of Rotterdam. Paper presented at the European Simulation Multiconference, Modelling and Simulation 1995, Prague, Czech Republic.

van Heemst, C. (2013). Channel capacity simulation - Advice report. Rotterdam: Royal HaskoningDHV.

130

APPENDICES

Appendices 132

A EXAMPLES OF MARINE OPERATIONS AT THE PORT OF JEBEL DHANNA/RUWAIS

Real examples of The Port of Jebel Dhanna/Ruwais operations retrieved from MarineTraffic (2014) are given at the following figures. Not all situations are covered in this Appendix and neither the tide nor the terminal of call of the incoming vessels is known. Therefore, exact conclusions on the cause of waiting times cannot be drawn.

Figure A-1 presents the most fortunate condition. A tanker arrives at the port, sails through the Main Access channel, then sails through the Deep Water channel and berths at one of the Jebel Dhanna Terminal’s SPM. This situation corresponds to the ideal incoming vessel process: without any delays.

Figure A-1 Arrival process of a Tanker with no delays (MarineTraffic, 2014).

Differently from the first situation, in Figure A-2 the arrival process of the Tanker is delayed: the vessel sails through route “a” and goes to the outer anchorage when getting to the Ghasha buoy. Since there are no tidal windows for incoming vessels the delay must be caused by traffic restrictions in both routes “f” and “g”.

The destination of the vessel after leaving the outer anchorage can be either the berth or the inner anchorage depending on the berth availability. In Figure A-3 a vessel shifting from the outer anchorage directly to the berth has occurred.

Appendices 133

Figure A-2 Arrival process of a Tanker with delay (MarineTraffic, 2014).

Figure A-3 Vessel shifting from outer anchorage to berth.

Appendices 134

Since outgoing vessels have priority and can block routes “f” and “g” for incoming vessels, whenever there is an opportunity for an incoming vessel to pass through these sections the chance is taken, even when the berth is still not available. Figure A-4 presents this situation, when shifting of a cargo vessel from the outer to the inner anchorage occurs.

Figure A-4 Shifting of a cargo vessel from the outer to the inner anchorage.

In Figure A-5 a tanker is shifted from the berth to the inner anchorage probably due to the insufficient tide for the vessel to leave the port. Since outgoing vessels have priority, only exogenous activities, such as tides, should be able to hamper the sailing.

Figure A-5 Vessel shifting from berth to inner anchorage.

Appendices 135

Figure A-6 shows a sequence of events. Vessels A and B are leaving the port while vessel C was waiting at the outer anchorage to enter the port. As soon as vessel A crosses the one-way section (S5), vessel C, which was previously at the anchorage, have clearance to sail in. With a draught of 6m vessel C can sail either through S6 or S7, however vessel B is sailing outwards through S6. Thus, vessel C enters the port via S7.

Figure A-6 Traffic rules at one-way sections.

Appendices 136


B FLEXSIM CONCEPTS The following FlexSim concepts are presented in this Appendix: Ports, Messages, Order of Events, Triggers, Labels, Global Tables, Global Variables, Tracked Variables, Dashboards and User Commands.

Ports

Objects in FlexSim are connected to each other via their Ports. Input and Output Ports are used in the model in the routing of Vessels. For example, FixedResources receive flowitems through their Input Ports, do something to the flowitem, then release the flowitem through their Output Ports. Routing can be done by programming the SendtoPort function of the upstream object (Output Port) or the Pull requirement of the downstream object (Input Port).

As an example, the berth receives the vessels according to its pull requirement then holds the vessel while the quay operations are taking place then releases the vessel through one of the Output Ports.

The order of the Port connections is important. For instance, if an anchorage (queue) is connected to all berths of a terminal (processors) the availability of berths is checked from the first to last Output Ports (Figure B-1). Consequently the berths are filled by vessels from the first to the last one in the list which means that if Berth 3 is never used there were never 3 vessels at the same time willing to enter that terminal. Additionally, if two vessels are ready at the same time, for instance in Berth 1 and 2, the one which is at Berth 1 goes Out of Harbour first (the word Harbour is used here to avoid confusion).

Figure B-1 Example of output and input Ports connections.

Messages

Objects that are not connected to each other can communicate via messages. Messages contain a sender, a receiver and when required additional parameters such as values, references to other objects and so on. This function is used for instance when vessels are requesting clearance to the Harbour Master, which receives the message and based on the location of the vessel and the vessel’s characteristics, calculates the vessel timetable and gives/or not clearance.

Order of Events

The concept of a discrete event simulation tool was already presented in paragraph 3.2. An event list can be assessed any time during the simulation. The more complex a model is, the greater the number of events present at the list. The list is not complete when a model starts running. Certain

Berth 1

Input port 1: AnchorageOutput port 1: Out of

Harbour

Anchorage

Output port 1: Berth 1Output port 2: Berth 2Output port 3: Berth 3

Berth 2


Harbour

Berth 3


Harbour

Out of Harbour

Input port 1: Berth 1Input port 2: Berth 2Input port 3: Berth 3

Appendices 137


events can trigger other events which are added to the list and so on. There is a certain order of events to happen in the model. A simplified example of a Berth’s (processor) order of events is presented in Figure B-2. The same figure is also used at the explanation of the following concept: the triggers.

Figure B-2 Processor simplified order of events.

Triggers

Each object has a different set of triggers related to it. The ones that are present in most of the objects are: OnReset, OnMessage and OnEntry/Exit. Figure B-2 presents some of the triggers of a processor, in the case of a source an OnCreation trigger also exists, a networknode has also an OnContinue trigger and so on. Triggers are fired at different moments during the simulation. Whenever a trigger is called the function that is written on it is executed. For illustration purposes an example is described and presented in Figure B-3. If all Input Ports of an object (2) have to be closed for some reason when a flowitem enters another object (1) the following procedure can be done. A message is sent to object 2 by firing the OnEntry trigger of object 1. When object 2 receives the message its OnMessage trigger is fired executing the function closeinput.

Figure B-3 Example of the use of triggers and messages.

The model itself also has triggers, for instance OnReset of the model tables can be filled or cleared, tracked variables can be initialized, and libraries can be loaded.

By using triggers, all kinds of programmed functions can be executed in specific moments of the simulation marked by events. Therefore, understanding the order that events happen and the functioning of triggers is essential for setting up a successful model.

On Entry Trigger

Process Time

Setup Time

On Process Finish Trigger

Send To Port On Exit Trigger

Vessel enters the Berth

Vessel enters next object

On Setup Finish Trigger

On Entry Trigger :

sendmessage to Object 2

Flowitem enters Object 1

On Message Trigger :

close input

Object 2 receives the message

Appendices 138


Labels

Labels are used to store data on objects and assist decisions to be taken in the model. For instance, vessels from a given class are created at the source and contain the labels and initial values established at the FlowItem Bin for that class. Those label values can be changed by specific events, such as triggers and new labels can also be added during the simulation. An example of Label use is having the SendToPort or Pull Requirement decisions based on a label value. Using the same scheme of Figure B-1 as an example, vessels could have a label called Berth with a value ranging from 1 to 3 and the anchorage would be able to SendToPort 1 (Berth 1) only vessels with the label Berth equal to 1. The same result is achieved by setting the Pull Requirement of Berth 2, for instance, to Specific Label: Berth, with a value 2. The variety of applications of labels is incredibly great.

Global Tables

Global Tables are used to store numerical or string data. This data can be accessed or modified by any model object. Values can be entered manually, previous to the simulation start, to be accessed by objects during the simulation, or can be filled up by the objects during the simulation to store relevant information.

Global Variables

The value of a Global Variable, in the same way as Global Tables, can be accessed or modified by any object at any time during the simulation. The type of the variable can be either an integer, a double precision floating point, a tree node (reference to an object) a string or an array of any of the preceding types. Variables that are used at different times by different objects should be set up as global variables.

Tracked Variables

Tracked variables are used for recording output data. The data is stored in two columns, the first one representing the time and the second one the variable value at that specific time. Tracked variables can be directly graphed as line or dot charts in the Dashboard.

Dashboards

Dashboards are used to monitor graphs and statistics as the model runs. Examples of statistics that can be monitored are the number of vessels at the anchorages, the utilizations of the berths, tracked variables etc.

FlexScript

FlexScript is the scripting language used to customize triggers and other parameters of the model. It is nearly identical to C++ in its syntax and application but it simplified for ease of use. Several FlexScript commands are available for different purposes such as referencing objects, accessing assigning and managing data in tables and many others.

User Commands

In addition to the predefined FlexScript commands, user commands can also be added to the model. Once a user command is created, it can be called anywhere and anytime in the model. One of the advantages of having a user command set up is: when you have many objects executing the same code and you use a user command for that, when a change is required in that code, the modifications have to be performed only once and not at every single object at a time. In this way the model gets more organized and mistakes are avoided.

Appendices 139


Appendices 140


C SIMULATION MODEL INPUT DATA

Figure C-1 Definition of routes and limit speeds.

Vessel max speedVessel max speed reduced by 2 knots (bends)8 knots6 knots5 knots3 knots8 knots for outgoing tidal bounded vessels

Appendices 141


Table C-1 Inter-arrival times in minutes – IAT Global Table.

DESTINATION 2011 2014 2016 2022 2030 01 Jebel Dhanna Terminal – ADCO (Crude oil) 4380 4380 4380 4380 4380 02 Ruwais Refinery Terminal – TAKREER (DW) 1168 756.3 565.2 565.2 565.2 03 Ruwais Refinery Terminal – TAKREER (CT) 13140 9556.4 7508.6 7508.6 7508.6 04 GASCO – Ruwais (LPG and Paraffinic Naphtha) 1314 876 700.8 700.8 700.8 05 FERTIL – Ruwais (Urea, Ammonia, Ethylene) 4778.1 5256 5256 5256 5256 06 GASCO – Ruwais (Sulphur) 0 7508.6 7508.6 7508.6 7508.6 07 BOROUGE (Polyethylene) 1010.8 2021.5 2021.5 2021.5 2021.5 08 GASCO – Ruwais (Sulphur) 2 0 7508.6 7508.6 7508.6 7508.6 09 ChemaWEyaat - Liquids and gas (excl LNG) 0 0 4778.2 1051.2 1051.2 10 ChemaWEyaat - LNG and FRSU 0 0 0 0 10108 11 ChemaWEyaat - Bulk 0 0 0 17520 17520 12 ChemaWEyaat - Container 0 0 0 6570 1194.6 13 ChemaWEyaat - Construction 0 0 10512 10512 10512

Table C-2 Example of distribution of vessels classes per destination– Traffic_2030 Global Table.

Destination /Vessel Class 01 02 03 04 05 06 07 08 09 10 11 12 13

BV01 0 0 0 0 0 0 0 0 12 0 0 0 0 BV02 0 0 14 0 0 7 0 7 11 0 0 0 0 BV03 0 0 86 5 0 21 0 21 17 0 0 0 100 BV04 0 12 0 14 0 29 0 29 7 0 0 0 0 BV05 0 12 0 15 100 29 0 29 45 0 100 0 0 BV06 0 17 0 33 0 7 0 7 8 0 0 0 0 BV07 0 16 0 33 0 7 0 7 0 0 0 0 0 TK01 0 38 0 0 0 0 0 0 0 0 0 0 0 TK02 100 5 0 0 0 0 0 0 0 0 0 0 0 CF01 0 0 0 0 0 0 75 0 0 0 0 0 0 CF02 0 0 0 0 0 0 0 0 0 0 0 14 0 CV01 0 0 0 0 0 0 0 0 0 0 0 86 0 RR01 0 0 0 0 0 0 25 0 0 0 0 0 0 LN01 0 0 0 0 0 0 0 0 0 100 0 0 0

Table C-3 Minimum required tide and channel selection for each vessel class.

Minimum required tide indicated in ()

IN BALLAST (incoming) LADEN (outgoing) f (DW Channel) g (Stewart) f (DW Channel) g (Stewart)

Least depth (CD) 14 9.9 14 9.9

Bulk vessel

01 - - - - 02 - x - x 03 - x - x 04 - x - x 05 - x o (2.6) 06 - x o (3.6) 07 - x o (3.6)

Tanker 01” - x o (1.3) (5.4) 02” * (0.6) o (1.3) (5.4)

Container Feeder 01 - x - x 02 - x * (1.2)

Container Vessel 01 o (2.3) o (1.0) (5.1) RoRo vessel 01 - x - x LNG Carrier 01 * (1.5) o (4.1)

XX” indicates that vessels are laden to 14m draught and not to its maximum.

- the vessel has no depth restrictions (BV01 uses the Relief Route) x both channels can be used but preference is given to the Stewart Channel * the given channel is selected but the other option could be used if a tidal window was applied o the other channel cannot be used neither when considering a tidal window

(X,X) minimum required tide for the vessel to sail through the channel

Appendices 142


Figure C-2 Relative number of vessels per class.

Table C-4 Initial label values for all vessel classes.

LABEL NAME / Vessel Class

ID Pilotage

Exemption Certificate

Destination

Total Cargo

Percentage Cargo Outgoing DW_in DW_out Tidal

bounded

BV01 0 1 0 3000 1 0 0 0 0 BV02 0 0 0 5000 1 0 0 0 0 BV03 0 0 0 15000 1 0 0 0 0 BV04 0 0 0 20000 1 0 0 0 0 BV05 0 0 0 40000 1 0 0 1 0 BV06 0 0 0 50000 1 0 0 1 0 BV07 0 0 0 60000 1 0 0 1 0 TK01 0 0 0 120000 0.875 0 0 1 1 TK02 0 0 0 200000 0.75 0 1 1 1 CF01 0 0 0 1100 1 0 0 0 0 CF02 0 0 0 1200 1 0 0 1 0 CV01 0 0 0 5000 1 0 1 1 1 RR01 0 0 0 1100 1 0 0 0 0 LN01 0 0 0 90000 1 0 1 1 0

Appendices 143


Table C-5 Berth productivities.

DESTINATION PRODUCTIVITY

1 Jebel Dhanna Terminal – ADCO (Crude oil) 5500 T/hour 2 Ruwais Refinery Terminal – TAKREER (Deep water) 2500 T/hour

3 Ruwais Refinery Terminal – TAKREER (Coastal tankers) 810 T/hour

4 GASCO – Ruwais (LPG and Paraffinic Naphtha) 2000 T/hour 5 FERTIL – Ruwais (Urea, Ammonia, Ethylene) 500 T/hour 6 GASCO – Ruwais (Sulphur) 1300 T/hour 7 BOROUGE (Polyethylene) 50 moves/hour

7 (RoRo) BOROUGE - RoRo 10 hours 8 GASCO – Ruwais (Sulphur) 2 1300 T/hour 9 ChemaWEyaat - Liquids and gas (excl LNG) 1250 T/hour

10 ChemaWEyaat - LNG and FRSU 2000 T/hour 11 ChemaWEyaat - Bulk 1300 T/hour 12 ChemaWEyaat - Container 100 moves/hour 13 ChemaWEyaat - Construction 10 hours

Appendices 144


D SIMULATION MODEL VERIFICATION Table D-1 All Vessels Data output table

ID

Vess

el_C

lass

Tida

l_bo

unde

d

Dest

inat

ion

Tota

l_Ca

rgo

Perc

enta

ge_C

argo

Entr

y_Ti

me

Arriv

al_G

HASH

A

Depa

rtur

e_G

HASH

A

Saile

dRou

teIn

Bert

hing

Extr

atim

e

Serv

iceT

ime

Unb

erth

ing

Tota

l_tim

e_at

_ber

th

Saile

dRou

teO

ut

Exit_

Tim

e

Turn

arou

nd

WT_

OA_

Traf

fic

WT_

OA_

Spac

ing

WT_

IA_B

erth

WT_

IA_T

ide

WT_

IA_S

paci

ng

WT_

TOTA

L

227 BV07 0 2 60000 1 32594 32841 32841 7 46 149 1440 30 1665 6 35067 2472 0.0 0.0 0.0 0.0 0.0 0.0 228 BV07 0 4 60000 1 32982 33228 33346 7 45 113 1800 31 1989 6 35891 2909 117.9 0.0 0.0 0.0 0.0 117.9 229 TK01 1 2 120000 0.875 33986 34233 34360 7 32 121 2520 57 2730 6 37759 3773 127.3 0.0 0.0 102.8 0.0 230.1 230 TK01 1 2 120000 0.875 34018 34265 34390 7 47 144 2520 44 2755 6 37789 3771 95.0 30.0 0.0 48.8 30.0 203.8 231 BV01 0 9 3000 1 34209 34347 34347 0 37 119 144 30 330 0 35050 840 0.0 0.0 0.0 0.0 0.0 0.0 232 BV07 0 4 60000 1 34431 34678 34678 7 52 99 1800 52 2003 6 37234 2802 0.0 0.0 0.0 0.0 0.0 0.0 233 BV07 0 2 60000 1 34550 34797 34806 7 43 109 1440 55 1647 6 37032 2482 8.8 0.0 0.0 0.0 30.3 39.1 234 BV07 0 4 60000 1 34673 34920 34993 7 52 143 1800 50 2045 6 37593 2920 73.7 0.0 0.0 0.0 0.0 73.7 235 BV05 0 5 40000 1 34708 34954 35023 7 35 98 4800 56 4989 6 41809 7101 39.1 30.0 1224.8 0.0 17.9 1311.8 236 LN01 0 10 90000 1 34987 35234 35234 6 42 108 2700 33 2883 6 38698 3710 0.0 0.0 0.0 0.0 0.0 0.0 237 BV04 0 9 20000 1 34992 35197 35197 7 43 99 960 30 1132 7 36814 1822 0.0 0.0 0.0 0.0 0.0 0.0 238 BV07 0 2 60000 1 35030 35277 35294 6 50 90 1440 43 1623 6 37481 2451 0.0 17.0 0.0 0.0 0.0 17.0 239 BV04 0 9 20000 1 35053 35258 35266 6 56 103 960 57 1176 7 36943 1889 0.0 18.7 0.0 0.0 0.0 18.7 240 BV06 0 2 50000 1 35384 35631 35631 7 41 108 1200 56 1405 6 37623 2239 0.0 0.0 0.0 0.0 26.2 26.2 241 CV01 1 12 5000 1 35415 35662 35662 6 46 112 3000 53 3211 6 39470 4054 0.0 0.0 0.0 0.0 0.0 0.0 242 BV04 0 4 20000 1 35618 35823 35823 7 40 90 600 46 776 7 37072 1454 0.0 0.0 0.0 0.0 0.0 0.0 243 CV01 1 12 5000 1 35715 35962 36080 6 50 113 3000 43 3206 6 40589 4874 118.2 0.0 0.0 706.2 0.0 824.4

Appendices 145

Appendices 146


E WAITING TIMES CORRELATION GIVEN THE CAUSE OF DELAY

Figure E-1 Correlation between waiting times given their cause from year 2014 to 2030.

Appendices 147


Figure E-2 Correlation between waiting times given their cause for the Hypothetical Traffic Increase scenarios.

Appendices 148


Figure E-3 Correlation between waiting times given their cause for the Measures to Reduce Congestion.

Appendices 149

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