Computational Logistics: Examples from Stefan Voß, IWI Container Terminal Operations · ·...

Stefan Voß, IWI 09 November 2011 Computational Logistics: Examples from Container Terminal Operations

Stefan Voß ([email protected]) Institut für Wirtschaftsinformatik (Information Systems), University of Hamburg

HongKong, May, 29th 2012

Introduction and Motivation History and Background

Terminal structure and handling equipment Terminal logistics and optimization methods Scalability and Research Issues

Container Terminal Operations and Scalability: Efficiency means Quality

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1. Introduction and Motivation History and Background

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Computational logistics concentrates on methods and information systems that

support or perform the planning and control of

logistics systems and processes.

Computational Logistics

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Computational Logistics involves the use of information systems and

modern information and communication technology (IT) for the design, planning and control

of logistics networks as well as the complex tasks within them.

Computational Logistics

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Containerization Trend: High Growth of Container Turnover

Data sources: UNCTAD - Review of Maritime Transport; Drewry Shipping Consultants; Clarkson Research Services

Container port turnover is growing much faster than the world trade, world GDP or seaborne trade.

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Competitiveness of a container seaport

Main success factors: time in port for ships (transshipment time) combined with low rates for loading and discharging reliability Crucial competitive advantage: rapid turnover of the containers reduction of the transshipment time reduction of the costs of the transshipment process overall objective with respect to terminal operations: efficient usage of all resources minimization of the time a ship is at the berth!

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2. Terminal structure and handling equipment

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Operation areas of a seaport container terminal and flow of transports

Truck and Train Operation Area

Yard Moves

Ship Operation Area

YardImport/Export Stock

Empty Stock

Sheds

Quayside Operation

Hinterland Operation

Waterside Transshipment Process (WTP)

Landside Transshipment Process (LTP)

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Example of a partially automated container terminal (Brisbane, Australia)

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Example of an automated container terminal (Container Terminal Altenwerder (CTA), Hamburg, Germany)

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Waterside Transshipment Process

Incoming ships are assigned to a berth. Containers are cleared by cranes (bridges) at the ship. Special vehicles (e.g. straddle carriers) perform all transports within the yard and to the ship. Logistic problems

berth planning stowage planning yard planning sequencing of all containers (loading and unloading sequence) assignment of (transshipment) containers to straddle carriers

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Storage Area – Handling Equipment – Cranes Double/Triple Rail Mounted Gantry Cranes (DRMG, TRMG)

Cranes operating in storage area Rail mounted gantry cranes (RMG)

stable new development: Double RMG

consists of two RMGs of different height and width able to pass each other avoiding a handshake area slightly higher productivity of the system

man-driven or automatic span up 8-12 rows

allow for stacking 4-10 containers high technical performance: ~ 20 moves/h

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Horizontal transport means

Reachstackers and forklifts/sidelifter

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Automated guided vehicle

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Straddle carriers

Handling Equipment

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Container terminal systems

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Container terminal systems

schematic side view

Is this a Supply Chain ?

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3. Terminal logistics and optimization methods

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Solution

New Real-world Problem Research

“This profession [OR] has presently more algorithms than applications ...”

[Wolsey, 1979]

“No systems, no impact” [Nievergelt, 1994]

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Basic structure of handling procedures for container flows

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Methods /Algorithms

Assignment problems (exact solution) Vehicle routing problems (incl. TSP, TDTSP; simple heuristics) MIP models (reference solutions, subproblems; standard solvers) Online optimization (simple priority rules)

• Characteristics of container terminal operation demand online (real-time) optimization and decision.

• Reason: Most of the processes occurring at container terminals cannot be foreseen for a longer time span – in general the planning horizon for optimization is very short.

Methods: Heuristics, Metaheuristics, (Discrete Event) Simulation etc.

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CTA at a Glance Overview: Remotely Controlled Transfer

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4. Scalability and Research Issues

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Scalability on a small scale (vs. Scalability on a large scale) Double Rail Mounted Gantry Cranes (DRMG)

Allow for stacking 4-10 containers high Technical performance: ~ 20 moves/h Net productivity ?? The more we utilize the space the more we need to account for making the right decisions where to locate containers. That is, for bad locational decisions we might have to relocate.

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The Blocks Relocation Problem (BRP)

Given A bay with n blocks which have to be retrieved Assumptions (Kim and Hong, 2006) Retrieval order is given (1, 2, …, n) LIFO Only the upper blocks may be moved No pre-marshalling Objective Retrieval order such that the number of relocations is minimized

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The Blocks Relocation Problem (BRP) Assumptions discussion

Assumptions (Kim and Hong, 2006) Only the upper blocks may be moved No pre-marshalling

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The Blocks Relocation Problem (BRP) Dynamic Programming Formulation

State variable: s = (k, i, t,C) k ∈ {1, . . . , n} is the block to be retrieved i ∈ {1, . . . ,m} is the stack in which the target block is found t is the list of blocks above the target block C is the configuration of the remaining blocks (Example: k = 1, i = 2, t = {5, 4}, and C = {{3, 2} , {7, 6}})

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The Blocks Relocation Problem (BRP) Dynamic Programming Formulation

State transition function T(s, x): Let s′ = (k′, i′, t′,C′) be the state obtained by

applying decision x ∈ D(s) to the current state s.

s′ = T(s, x) (i) t = ∅: k′ = k + 1, i′ is the stack in which

block k+1 is currently located, t′ is the list of blocks above k + 1 and C′ = C is the configuration of the remaining blocks

(ii) k′ = k, i′ = i, t′ = t \ {τ}, and C′ depends on the application of move x to block τ (e.g., with respect to the figure let us suppose that x = 1 s′ = T(s, 1) = (1, 2, {4} ,C′), where C′ = {{5, 3, 2} , {7, 6}})

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Dynamic Programming

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Move-based vs. Method-based Neighborhoods (Paradigms?)

Move-based neighborhoods: Neighborhood search based on topological concepts relating a given solution to similar solutions via local changes or moves. (Example: small homogeneous neighborhoods, very large scale neighborhoods) 1. Define a neighborhood 2. Find efficient methods for its exploration

Method-based neighborhoods: The basic structure of a neighborhood is determined by the needs and requirements of the method used to search it.

1. Assume a given method. 2. Define a neighborhood that suits the method.

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The Corridor Method (CM) Sniedovich/Voß (2006)

The CM can be described as a local search method where neighborhoods are relatively large sets whose structure and size are compatible with the optimization method operating on it. • The optimization problem under consideration is large. • There is an optimization method for efficiently solving smaller instances of

the problem. • It is easy to generate (an initial) feasible solution. • There is an efficient method for generating suitably large neighborhoods

around feasible solutions to the problem on which the optimization method can be used.

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The Corridor

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Numerical Results

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A New Talk ?

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Extending towards Pre-Marshalling – Numerical Results

Only very few benchmark instances available. Example: Y. Lee and S.-L. Chao. A Neighborhood Search Heuristic for Premarshalling Export Containers. European Journal of Operational Research. doi:10.1016/j.ejor.2008.03.011.

31 (Lee/Chao) 19 (CM) 15 (optimal)

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Scalability Scalability on a large scale (vs. Scalability on a small scale)

Technology combinations Horizontal transport -

quay Container yard

operations Horizontal transport -

rail TTU RTG TTU

AGV Auto. RMG TTU Mini SC Auto. RMG TTU

pure SC system (incl. truck handling)

Typical system characteristics RTG + TTU: high yard capacity / high stuff require. / flexible in peaks

RMG + AGV: high civil work invest. / high degree of auto. / low staff require. RMG + Mini SC: high civil work invest. / auto. yard operations / reduced staff requ. Pure SC system: poor yard capacity / high stuff require. / flexible in peaks

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…is frequently quantified by Terminal Cost (cost / box) and Handling Capacity (max. throughput / time)

Technical and functional attributes

Application Conditions Terminal Organization

Competitiveness of Handling Technology

e.g., stacking height, transport speed,

energy consumption, manning requirements,…

e.g., level of wages and energy prices,

inflation rate, terminal space,

length of quay wall…

e.g. organizational concepts, methods for equipment control,

human experiences,…

REGION-related LEVEL

TERMINAL-related LEVEL

Scalability

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General aspects of investigation:

Equipment variants: TTU+RTG // AGV+RMG // SC+RMG // pure SC

Replacement of equipment and civil work components by technical lifetime

Terminal processes: Quay crane operations Horizontal transport at quay- and landside (excl. MTs) Container handling in the yard Truck handling Investment in equipment and civil work Operations cost: Labor cost Energy cost M&R cost Capital cost Terminal capacity

Scalability

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Basic assumptions (collaboration with Jürgen Böse):

„common organized“ terminal resources + modern equipment pieces (state of the art)

… further assumptions: equipment productivities, space requirements for yard stacks, energy consumption, staff requirements, etc.

Planning horizon: 25 years

quay wall length: 800m; capacity: 1.47 mill TEU (9 STS)

Yard: area 800m (parallel to quay), 500m depth

capacity: RTG (2.78 mill TEU), RMG (2.48 mill. TEU), pure SC (1.94 mill TEU)

=> Terminal throughput is limited by quay wall

Scalability

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Conditions of Central Europe …relating to wages, prices for energy / equipment / M&R services, inflation rate (3%), capital cost) Terminal capacity is equal for all equipment variants

Region-related competition level is measured by cost/box RMG yard systems deliver best results!

Total Cost per Box (25 years)

110%

83%100%96%

0%

20%

40%

60%

80%

100%

120%

RTG + TTU RMG + AGV RMG + Mini SC pure SC

Scalability

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Condition changes: 5% of European wage level (e.g. less developed countries) …competitiveness changes! RTG system takes „pole position”

Total Cost per Box (25 years)

83%

100%110%

96%100%

109%106%

88%

0%

20%

40%

60%

80%

100%

120%


Scalability

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Yard area is halved => terminal throughput is limited by yard

Region-related competition level is measured by yard capacity

RTG system offers best yard efficiency

Yard Capacity(limited by yard)

128% 128%

100%

143%

90%

105%

120%

135%

150%


-15%-points

-28%-points

Scalability

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Summary / Hypotheses

Container turnover is expected to continue to grow. The size of container vessels will increase. Shortages in capacity at terminals and in surface distribution networks are viewed as the main constraints to current and future growth in containerized trade. There are also capacity issues with inland transportation by rail and truck. Government and port community stakeholders may present additional obstacles to capacity enhancement (road and rail infrastructure, security, environmental protection and safety). It is a challenging area for joint work in practice and research – let’s face it.

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Post them towards

[email protected]

Questions? Answers?

mailto:[email protected]



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Green Maritime Transport and

Environmental Paths to 'Green Logistics'

Air Pollution (Emissions) Oil Spill Slow Steaming

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Slow Steaming

Slow Steaming: 21 ± 1 Knots

Extra Slow Steaming (ESS): 18 ± 1 Knots

Super Slow Steaming (SSS): 15 ± 1 Knots

On 45 loops 445 container vessels (> 4.000 TEU) were operated under ESS in 01.2010

cf. Maersk Line (2009)

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Slow Steaming

cf. Notteboom (2009)

- €

20,000.00 €

40,000.00 €

60,000.00 €

80,000.00 €

100,000.00 €

120,000.00 €

3.000 TEU 5.000 TEU 8.000 TEU 10.000 TEU

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Environmental Paths to 'Green Logistics'

International Maritime Organisation (IMO) Base: Intergovernmental Maritime Consultative Organisation (IMCO) The IMO aims at controlling all issues related with merchant shipping (except pure commercial aspects), reducing the pollution of the sea as well as improving safety and conditions of work. The IMO collaborates with other UN organisations as well as with NGOs. For example, the IMO enacted the International Convention for the Prevention of Pollution of the Sea by Oil (OILPOL, 1967) resulting in protected zones. No ship is allowed to drain oil-containing waste water/alloy inside of those zones. However, the IMO has no executive power. MARPOL as Guideline The major international convention with respect to green maritime transport is the International Convention for the Prevention of Pollution from Ships (MARPOL Convention). It is a combination of two treaties adopted in 1973 and 1978. Currently, the convention includes six technical annexes: I - Regulations for the Prevention of Pollution by Oil, II - Regulations for the Control of Pollution by Noxious Liquid Substances in Bulk, III - Prevention of Pollution by Harmful Substances Carried by Sea in Packaged Form, IV - Prevention of Pollution by Sewage from Ships, V - Prevention of Pollution by Garbage from Ships, and VI - Prevention of Air Pollution from Ships. Annex VI was subsequentliy introduced in 1997 since the public and politics were not reasonably aware of air pollution caused by emissions not till then.

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Technical Aspects for Green Maritime Transport

Green logistics in maritime transport aims at reducing negative impacts of maritime traffic on environment and nature. Maritime traffic and transport offers a broad range of possible improvements by applying modern technical instruments as well as modern logistical concepts. Some technical means for reducing pollution in building and operating vessels can be directly applied, some require a modication ('retrofitting') of the ship, and some others can only be applied for newbuilding of ships.

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Some innovations “will make it”

Think of possible innovations! Skysails? Foldable Containers? Others?

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Enercon?

Sailing rotors use wind energy to propel the ship and works on the the principle of the Magnus effect named after a German physicist.

http://en.wikipedia.org/wiki/Magnus_effect

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Solar panels ? For instance, on Auriga Leader. http://www.allcarselectric.com/image/100225887_solar-panels-on-auriga-leader

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Empty Container Management: Foldable Container? Leanbox

Source: Leanbox GmbH

Idea: automated (dis)assembling

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IMO: Stepwise reduction of maximum permissible sulphur concentration in marine fuel

Since October 2008, IMO enactments show limit values such that emissions of NOx and SOx have to be reduced gradually until 2020. Rules for limiting the emitted amount of, e. g., SOx directly require complex and costly measuring instruments aboard. Alternatively, the amount could be calculated based upon ship's characteristics but this approach requires complex documentation and would be inaccurate. Therefore, the IMO enactments aim at a regulation of the sulphur content of the fuel. (Sulphur) Emission Control Area ((S)ECA)

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Management Science Issues

Policy instruments aiming at a reduction of emissions such as CO2 caused by maritime transport can be broadly divided into two categories. Some instruments affect only investment decisions while others, e.g., fuel taxes or cap-and-trade systems, affect the running of vessels and investments in ships. In other aspects OR may help.

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Trade, Transport and Ports Container Flows

The explosion of trade in containerized maritime transportation is increasing pressure on the leading sea ports. Major challenges of the coming years: Trade imbalances, congestion, etc.

Europe North America

Asia

Asia, particularly China, is still leading the growth in cargo flows

Volume imbalances large export flows, but only small import flows

(but new developments in 2008/09 due to restructuring of economies; but no reversal)

Data source: UNCTAD - Review of Maritime Transport 2009

<East-West TOTAL West-East>

North America

Estimated cargo flows on major global containerized cargo routes (Million TEU 2008)

<16.7 27.2 10.5> <5.6 20.1 14.5>

<4.3 7.2 2.9>

~40.0 (2007, est.)

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Empty Container Repositioning Critical Factors

Root cause: Trade imbalance (geographic) Dynamic trade effects (demand changes over time; seasonality, etc.) Uncertainty (unpredictable effects, e.g., customer demands, container processing, weather, traffic…) Different container types Blind spots in transport chain (lack of data, in particular w.r.t. rail, truck, inland terminals…) Carrier’s operational and strategic practices (alliances, sharing/exchanging containers, etc.) Imbalances inside containers, in transport costs 20% of total container flows at sea are empty (worldwide) The proportion of empty containers is expected to be nearly 23% in 2015 Costs of repositioning: US$ 400 per empty container (2002, Drewry Shipping Consultants) Total costs per empty container (transport, handling): US$ 675 (Behenna 2001) Containers are empty more than 50% of their total cycle time (maintenance/repair, storage, transit)

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B. Kuipers, TNO - China, Containers, congestion, CO2 and chains July, 2007

Transport costs (June, 2007) : China Rotterdam: US$ 1,600 Rotterdam China: US$ 350

Imbalances…

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… not only in volume but also inside the containers and in transport costs

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Empty Container Time Series Forecasting Model Development – ABC-XYZ Analysis

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Objective: estimation of the need for empty containers (of a particular type) in particular areas Idea: Forecasting demand based upon past data Preliminary analysis of assortment of all time series (importance, predictability) based upon their characteristics Analysis of the complete time series set for prioritization of forecasting methods Structuring forecasting approach to align data & suitable methods

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Empty Container Time Series Forecasting Model Development – Time Series Analysis

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Congestion effects Calendar effects for Asia (example major shipping company) • Chinese New Year • Christmas & Gregorian New Year • Chinese National Holiday (different lead & lag per year) • Japanese Day of the Children (different lead and lag per year) •… Encoding of many calendar effects for different regions Statistical testing of significance of calendar effects

outlier

outlier

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Trade, Transport and Ports Issues

Issues of leading sea ports (not exhaustive) Strong growth in containerized cargo

Congestions (US, Europe)

(radical) Innovations?

Highly fragmented industry with a large number of agents Precise information for planning as well as operations is needed 9/11 Increased pressure to comply with international security requirements

Current global economic crisis: even more pressure for improvements

in order to increase efficiency and reduce costs

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Perspective of seaports >2050 (?) and summary

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• less congestion • more automated; employing less port workers (+ shift of skills that are needed in order to control automation) • smoother trends (less driven by steep and upward growth patterns) • driven by new feedstock (e.g., gas, biomass) • less emissions (NOx etc.) • more dispersed (virtual port facilities, dry ports, hinterland activities) • more in line with common supply chain practises • on one hand: resemble ports of today quite a lot • on the other hand: might be based on totally new infrastructure

Computational Logistics: Examples from Stefan Voß, IWI Container Terminal Operations · ·...

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