Edited by Prof. Dr. Thorsten Blecker, Prof. Dr. George Q ... · Capacitated Multi-Depot Location...

Edited by Prof. Dr. Thorsten Blecker,

Prof. Dr. George Q. Huang and Prof. Dr. Fabrizio Salvador 8Operations and Technology Management

Management in Logistics Networks and Nodes

Concepts, Technology and Applications

Thorsten Blecker / Wolfgang Kersten / Carsten Gertz (Eds.)

erich schmidt verl ag

ES

Extract, for more details visit ESV.info/978 3 503 11227 2

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Management in Logistics Networks and Nodes

Concepts, Technology and Applications

Edited by

Thorsten Blecker, Wolfgang Kersten and Carsten Gertz

With Contributions by

Hatem Aldarrat, Mahmoud Ameri, Brahmanandan Anil, Amrinder Arora, Marie-Laure Baron, Thorsten Blecker, Lars Braubach,

Bülent Çatay, Tugrul Daim, Albert M. Douma, Tamer Doyuran, Matthew Ferguson-Calderon, Hitesh K. Gadhia, Carsten Gertz,

Mohammad Ghorbani Salanghooch, Alexander Goudz, Anna Granlund, Jos van Hillegersberg, Frank Himpel, Tarak A. Housein, Wolfgang Kersten, Herbert Kotzab, Elfriede I. Krauth, Winfried Lamersdorf, Matthias Lorenz,

Hipolito Martell Flores, Hervé Mathieu, Christopher McGinnis, Nils Meyer-Larsen, Hans M. Moonen, Rainer Müller, M. B. Nidhi, Bernd Noche, A. Norang, Evi Oktaviana, Otávio José de Oliveira,

Dhiren Patel, Heike Petri, Tiago Pinho, Selwyn Piramuthu, Alexander Pokahr, Wolfgang Renz, Abdolreza Rezaee Arjroody, Fathi Rhoma, Paulo César Chagas Rodrigues, Peter C. Schuur,

Kianoush Siamardi, Renato da Silva Lima, Ricardo Alexandre Soares, Claudine A. Soosay, Jan Sudeikat, Morteza Tolouei, Yu-Ju Tu, Luca Urciuoli, Thomas Will, Fatemeh Zahed, Hassan Zoghi,

Phanthian Zuesongdham

ERICH SCHMIDT VERLAG

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from: Blecker/Kersten/Gertz (Eds.), Management in Logistics Networks and Nodes.© Erich Schmidt Verlag GmbH & Co., Berlin 2008

V

Preface

Today, there is a huge variety of logistics networks – ranging from very simple locally oriented chains with only a few participants to internationally operating complex systems connecting firms all over the world. However, each of these networks is formed by logistics nodes (e.g. container terminals) connected by different types of edges. Various questions arise during the build-up and operation of these systems. In the design phase, partners have to be chosen, facilities have to be located, and connections have to be specified in a holistic way. On the operational level, logistics nodes have to be engineered, handling equipment has to be constructed, and last but not least, management systems have to be established in order to run and control operations within these networks. In recent years, logistics research has made extensive progress in the analysis of connections between different network nodes.

Therefore, this book focuses on the management of network interaction as well as on the improvement of logistics nodes themselves. In practice, this all-embracing approach provides the chance to optimize logistics nodes not only based on their own requirements, but to enhance them to meet network-wide demands. In consequence, it becomes possible to adjust node design and operation to the needs of the whole supply chain. Vice versa, in a holistic improvement process the network can be constructed in such a way that the requirements posed by the nodes are taken into account.

Information technologies are key enabler of logistics networks and nodes. New technologies in this field allow supply chains to increase their efficiency significantly and help to create further innovations in different areas. In this context, an important task is to find structures and approaches which enable all types of innovations in logistics networks and nodes for a better fulfillment of customer needs. Another challenge is to handle the growing complexity and the associated supply chain risks.

This volume provides a valuable insight into novel logistics services, innovative distribution and supply chain management, supported by service-oriented architectures and agency approaches as well as other pioneering technologies for logistics such as Auto-ID.


Preface

VI

We would like to thank the authors for their excellent contributions which advance the logistics research progress. Without their support and hard work, this volume would not be possible. Additional thanks are due to the publishing company, the Erich Schmidt Verlag, especially to Dr. Joachim Schmidt for the possibility to publish this volume and his valuable cooperation. This book would not exist without a good organization and preparation. We would like to thank Thomas Will, Philipp Hohrath, and Jan Koch for their efforts to prepare, structure, and finish this book.

Hamburg, August 2008 Thorsten Blecker, Wolfgang Kersten and Carsten Gertz


VII

Table of Content

Preface .............................................................................................................. V�

Table of Content ............................................................................................ VII�

I. Distribution

Rule Based Logistics Management for a Single Warehouse Multi Distributor System ............................................................................................. 3 Brahmanandan Anil and M. B. Nidhi

Lean Retail Logistics: What if We Focused on the Store? .............................. 15 Marie-Laure Baron

Characteristics and Structure of the International Container Port Network – An Analysis of Network and Node Design ................................... 33 Hitesh K. Gadhia and Herbert Kotzab

Designing and Improving Distribution Strategies in a Complex Distribution Logistical Network Using a Hybrid Simulation Modeling Approach [HSMA] .......................................................................................... 45 Tarak A. Housein

Distribution Logistics Network Planning: A Hybrid Metaheuristics Capacitated Multi-Depot Location Routing Model ......................................... 59 Hatem Aldarrat, Alexander Goudz, Matthias Lorenz, Fathi Rhoma, and Bernd Noche

A Model for Roads Pricing and Valuation in Iran .......................................... 81 Mahmoud Ameri, Fatemeh Zahed, and Abdolreza Rezaee Arjroody

Implementation of Location Strategy Tools .................................................... 97 Evi Oktaviana


Table of Content

VIII

II. Supply-Chain Management

A Study of Management of Intermediate Inventory in the Manufacture of Paper Products in Brazilian Companies .................................................... 109 Paulo Cesar Chagas Rodrigues, and Otávio José de Oliveira

Logistics Automation – an Enabler for Competing ....................................... 129 Anna Granlund

Supply Chain Management in the Brazilian Automotive Industry – Analysis and Improvements Proposal ........................................................... 147 Ricardo Alexandre Soares and Renato da Silva Lima

Managing Supply Chain Networks: Strategies for Logistics Integration ..... 163 Claudine A. Soosay

Project Cargo Standard Process for Logistics Service Provider: The Cimosa Approach .......................................................................................... 181 Phanthian Zuesongdham

True Cost of Outsourcing .............................................................................. 199 Christopher McGinnis, Tugrul Daim, and Matthew Ferguson-Calderon

Case Study of a Portuguese Supply Chain Management Model in Construction ................................................................................................... 211 Tiago Pinho

III. Agency and SOA Approaches

Using a Management Game to Exemplify a Multi-Agent Approach for the Barge Rotation and Quay Scheduling Problem in the Port of Rotterdam ..... 227 Albert M. Douma, Jos van Hillegersberg, and Peter C. Schuur

Two Enhanced Savings Functions for the Clark-Wright Algorithm ............. 245 Tamer Doyuran and Bülent Çatay

Service Oriented Architecture Enabling a Global Market-Place for the Supply Chain ................................................................................................. 259 Hervé Mathieu

Obstacles to Multi-Agent Systems Implementation Expert Opinions Confronted with Literature ............................................................................ 269 Hans M. Moonen, Albert M. Douma, Elfriede I. Krauth, and Jos van Hillegersberg


Table of Content

IX

Simulation and Implementation of Logistics Systems Based on Agent Technology .................................................................................................... 291 Alexander Pokahr, Lars Braubach, Jan Sudeikat, Wolfgang Renz, and Winfried Lamersdorf

Intelligent Transportation Systems Applications for Winter Maintenance ... 309 Hassan Zoghi, Kianoush Siamardi, and Morteza Tolouei

Developing a Mathematical Six Section’s Supply Chain Model with its Evaluation in Khorasan Razavi Iran Khodro Iranian Company .................... 319 Mohammad Ghorbani Salanghooch and A. Norang

IV. New Technology in Container Logistics

Benefits of Standardised RFID Transponders in Container Logistics .......... 335 Thomas Will and Thorsten Blecker

The Application of Radio Frequency Identification (RFID) in the Context of Ground Handling Processes at a Major International Airline ..................................................................... 353 Frank Himpel and Heike Petri

Applying Stochastic Capacity Management to Manage Truck Traffic around a Sea Port ........................................................................................... 365 Amrinder Arora and Dhiren Patel

RFID Supports SCEM in Container Transport Networks ............................. 377 Nils Meyer-Larsen and Rainer Müller

Addressing False RFID Reads in Supply Chains .......................................... 387 Yu-Ju Tu and Selwyn Piramuthu

DETCCM Networks Analyse Model Applied for Short-Sea-Shipping Opportunities Prospecting. Le Havre, Hamburg, Marseille and Valencia Port’s Cases ................................................................................................... 399 Hipolito Martell Flores

The Security Eco-System: How Supply Chains’ Players Affect Cargo Vulnerability .................................................................................................. 417 Luca Urciuoli

Authors .......................................................................................................... 437�

3

Rule Based Logistics Management for a Single Warehouse Multi Distributor System

Brahmanandan Anil and M. B. Nidhi

Abstract The delivery stage is the most expensive phase in distribution process of Supply chain and Logistics management. In this work, Logistics Management of a LPG bottling plant for distributing Gas cylinders to various distribution centers is undertaken. Unlike the traditional methods of Vehicle Routing Problem (VRP) where single tour per vehicle is employed, this work focuses on multi tour per vehicle. The customer demand pattern is obtained from the projections of previous years data. A simulation model has been prepared using MATLAB.7.0.1. to study the existing system in which random allocation of trucks is done. A performance measure using penalty points is used to compare the solutions. The losses due to unmet demand and maintaining idle resources are quantified by penalty points. The simulation results are in good agreement with the present system. A priority rule based allocation is suggested and simulations are undertaken to study the effect on cost involved and truck utilization. The results indicate 37% reduction in resource requirement for the suggested strategy. Keywords: Supply Chain and Logistics Management, Delivery Scheduling, Vehicle Routing, Distribution Planning, Simulation and Optimization.

1 Introduction Today’s challenge for a superior supply chain and logistics management system is to remain competitive by finding best fit of both efficiency and responsiveness. To achieve this, logistic systems must be significantly improved in terms of cost effectiveness and coordinated information flow in decision making. Logistics management took form from transportation management. Former, widely differs today with its activities in demand forecasting, capacity planning, selecting vendors and customers, purchasing, distribution and inventory management, and the latter deals with the different types of vehicle and the route to be followed by each vehicle to meet the customer demands in time. In supply chain logistics, the distribution stage is the most critical one and its importance is evident from the magnitude of the associated costs. Some surveys (Bodin et al., 1983; Anily, 1986)



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show that physical distribution costs account for about 16%-30% of the value of an item or even sales volume. There are three major problems why traditional Operations Research techniques are not enough to deal with this problem, which is known as the Vehicle Routing Problem (VRP). Firstly they are inherently combinatorial, secondly, complexities increase with increase in variables involved, and finally these methods are unable to account for dynamism of on-line distribution. Fleet routing and truck scheduling are two critical activities related to Distribution process. These attribute to the carrier’s profitability, its level of service, and its competitive capability.

The Vehicle routing problem (VRP) mainly deals with transport of items from depot to customer centers. It is applied to most of the real-world problems like the milk run fleet, mail delivery, school bus routing, solid waste collection, heating oil distribution, parcel pick-up and delivery, dial-a-ride systems, and many others. The conventional VRP is generally taken as a wider version of Travelling salesman problem (TSP). Given a network with a set of nodes, V, and a set of edges, E, connecting these nodes, TSP is the problem of finding the shortest cycle passing every node exactly once. When M cycles are used, the problem is called M-TSP. In M-TSP, one of the nodes is called the depot, and it has to be included in each cycle. In a real-world application, the fairly simple structure of M-TSP is often not enough to satisfactorily describe the assumptions of the problem to be solved. To be able to handle more complex situations, different restrictions are added to M-TSP, and thus turning it into VRP. The general VRP consists of finding a collection of cycles, henceforth called routes, such that the total cost of the routes is minimized. A common case is that the routes are used by vehicles to visit the customers and collect or deliver goods. The routes should be constructed in such a way that all nodes except the depot, should be visited exactly once, all routes start and end at the depot, and a set of restrictions are to be satisfied. In general, solving a VRP means to find the best route to service all customers using a fleet of vehicles.

Early contributions in VRP were concerned with single trip per vehicle. This concept was extended to multi trip per vehicle and had accessibility restrictions in Site Dependent Vehicle Routing Problem (SDVRP). Here, all vehicles could not visit all the customers, where the heterogeneity in fleet was introduced and a standard definition implicitly assume that each vehicle is used only once over the planning period (Tarantilis et al., 2005). Sung Hun Song et al. (2002) discuss the different algorithms for VRP, in the context of solving a newspaper logistics problem. Solomon et al.(1987), Russell et al.(1995), Bramel and Simchi-Levi(1996), Potvin et al.(1996), Taniguchi et al.(1998) have investigated vehicle routing problems with time windows (VRPTW). Other researchers have studied stochastic vehicle routing and scheduling problems (Jaillet and Odoni, 1988; Powell et al., 1995; Gendreau et al., 1996) and the dynamic VRP is discussed by E. Taniguchi et al. (2004). Advanced heuristics for VRP are discussed by Bodin et al. (2000). The bench mark problems are given by Cordeau et al. (2002), Toth. P et al.



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(2003), Tarantilis CD et al. (2002). Hokey Min. et al. (2002) provides a basis for modeling the supply chain.

In this work a Simulation model for Rule Based Logistics Management for a Single Warehouse Multi Distributor system is presented. The logistics of an LPG bottling plant is taken as an example to apply the proposed rule based strategy for distribution planning. The main objective is to study the effectiveness of the rule based strategy in order to arrive at optimum fleet size. The section 2 deals with problem formulation and describes the simulation procedure. In section 3 deals with problem formulation and section 4 the results of different strategies are compared based on the quantitative measures. In section 5 deals with conclusions and scope of future work.

2 The Problem The distribution system at an LPG bottling plant in South India is currently employing 48 trucks (of capacity 300 cylinders) to cater to 30 destinations. At present the trucks are loaded at random, without following any priority rule. In this work, a simulation model is developed using Matlab.7.0.1. to study the existing system. A priority rule based truck allocation is proposed and four different rules are studied. The following assumptions are made:

1. Ideal road and vehicle conditions exist 2. Loading/unloading at the depot during working hours only (8.30 am to 5.30

pm) 3. 26 working days in a month 4. No vehicle breakdowns / accidents 5. No scarcity of cylinder or fuel at the plant 6. Sufficient drivers/crew are available 7. No absenteesm 8. Each truck goes to only one distributor at a time. 9. A truck returns with an equal number of empty cylinders after delivery at

the distributor end. 10. The truck that takes more than 16 hours of travel will not be available for a

trip on the succeeding day. When there is less number of trucks, the demand of some destinations cannot be met and this results in reduction of revenue, in addition to losing the goodwill of the customer. On the other hand use of excess trucks may lead to idling of resources. Thus in order to evaluate the solution, the concept of penalty points is used to quantify/penalise the losses due to unmet demand, maintenance of idle trucks and the opportunity/goodwill lost. The solution with least penalty will be the best candidate.



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3 Formulation The demand pattern for a particular period is obtained from the previous data. The destination details, such as distances, travel time, details of demand, and rating of the distributor are given in Table 1. The demand pattern is converted to daily trips to destinations for the entire planning period and given as input (daily demand matrix). A sample demand matrix is shown in Table 2. The problem is approached as closed loop VRP (Ieke le Blanc et al., 2006) with no nodes in a route, and has the features of assignment problem (as there are n destinations with particular demand and a truck dispatched is supposed to return the depot with equal number of empty cylinders after delivery). The Objective function is

Where, i = 1...K, days, j = 1....S, destinations Pt Penalty for tripless trucks Pu Penalty for unmet demand ni Number of idle trucks Ct Cost for maintaining the truck per day Adij Demand Sdij Allocation for the destinations Rl Revenue lost per cylinder GLf Goodwill lost (considered as 20 % of revenue loss) Rm Merit rating of the customer based on demand (1-1.25) Note: for those trucks which are in use, the cost of maintaining the truck per day is covered up in profit. So penalty is applied only for idle trucks.

The truck allocation is based on dispatch rules, which may be random, simple rules, combination or weighted in nature. If we are giving a destination an urgent priority, it may be based on experience or based on convenience criteria or may be

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random. If the rules insist on definite conditions to be followed before each allocation it can be termed simple rules. If the allocation depends on two or more conditions to be checked each time it may be a combination rule. If the conditions are not comparable to quantify them, weighted rule are adopted. A set of dispatch rules in first two categories are tried out in our simulation. The effect of using the priority rule based truck allocation on penalty is analysed with following rules;

1. HDFS Highest Demand Farthest Destination first 2. HDNS Highest Demand Nearest Destination first 3. LDFS Lowest Demand Fartest Destination first 4. LDNS Lowest Demand Nearest Destination first

The simulation procedure is detailed in the pseudocode. Pseudo code ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Given the Daliy demand matrix and Destination details Intialise clock, numer of bays, truck number, number of simulations Initialise day =1, trips = 0 While day � end of month While daily-demand > 0 If time is within working hours Assign truck (at random / based on priority rules) trips = trips + 1 Update demand Else Break End of while (daily-demand) day= day+1 End of while (day) Evaluate the solution (based on penalty points) Plot necessary graphs Stop –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

4 Results and Discussion Three trial simulations of the existing scenario (with random allocation) has been carried out and the results are given in Figure1. It can be seen that the penalty is relatively high when number of trucks are very low or very high. In all three cases the minimum penalty is found to be while using around 40 trucks. The averaged penalty points for the three runs along with its trendline is shown in Figure 2. It can be seen that the number trucks required to meet the entire demand with minimum



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penalty is 45. This result is in agreement with the actual number of trucks being used by the organisation (presently 48).

Figure 1: Variation of penalty points with trucks used in random allocation The present model simulates the current scenario with good accuracy. The truck requirement is almost same as in real-life. Thus we can use the same model for applying dispatching rules.

Figure 2: Trendline of penalty ponts in random allocation The results of the simulation of allocation with despatching rules are given in Figure 3. The trucks required to meet the same demand is reduced to 30-36 when the priority rules are applied. The truck utilisation in each case is as shown in Figure 4. When the number trucks used were less than 15, the utilization of trucks are as high as 100%. However this results in unmet demand and losses. As the number of trucks used increases the utilization decreases. With dispatching rule the truck utilisation for minimum penalty is around 70%.

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Figure 3: Penalty values comparison for different despatch rules

Figure 4: Percentage truck utilisation The percentage of unmet demand in terms of total distance (in km) and trips are given in Figure 5(a) and Figure 5(b) respectively. Though the unmet demand decreases beyond 30 trucks, most of the time the trucks are idling.

Figure 5(a): Percentage unmet demand in terms of trips

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Figure 5(b): Percentage unmet demand in terms of km

The comparison of penalty points for different rules and minimum trucks required is given in table 3. For the same number of trucks, the penalty points were highest when the allocation is done at random. When we follow the priority rules, it has been found that there is considerable reduction in penalty while satisfying the same demand during the same cycle time. From the simulation analysis the best rule is LDFS which needs only 30 trucks to meet the entire demand.

The trucks required to meet the same entire demand in each of the following rule is given in Table 3. (Appendix 11)

5 Conclusion The resources being scarce or limited, it’s always a challenge to optimize their utilization in all spheres of life. The simulation model was able to realistically predict the present scenario. The results of priority rule based allocation clearly suggests the superiority of the proposed method over the existing practice. An improvement of nearly 37 % is seen with the priority rule LDFS. The number of trucks required reduced to 30 from 48, with an indication of cost reduction. This simulation substantiates the fact that rather than opting for random allocations, cost optimisation can be achieved through fleet sizing by following priority rules. The major limitation of this work lies in not considering many of the uncertainties like the vehicle break down, unavailability of drivers, cylinder/fuel scarcity, accidents, peak hour traffic etc. Analysis on stochastic nature of the scenario and optimisation of fleet size will be dealt in future work.

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^ Today, there is a huge variety of logistics networks – ranging from very simple locally oriented chains to inter-nationally operating complex systems. However, each of these networks is formed by logistics nodes connected by different types of edges. Various questions arise during build-up and operation of these systems.

This volume, edited by Thorsten Blecker, Wolfgang Kersten and Carsten Gertz provides a valuable insight into:

• new concepts for transportation and supply chain management

• latest findings in the area of distribution management

• service-oriented architectures and agency approaches for efficient and effective network and node management

• pioneering technologies for logistics such as Auto-ID.

With this book you will obtain information on how to plan and optimize logistics networks supported by modern IT and management concepts. You will learn how to manage existing nodes in different fields of logistics.

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69,00€(D)

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Edited by Prof. Dr. Thorsten Blecker, Prof. Dr. George Q ... · Capacitated Multi-Depot Location...

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