Journal of the Operational Research Society

, Volume 58, Issue 9, pp 1203–1213

Alternative algorithms for the optimization of a simulation model of a multimodal container terminal

Theoretical Paper

Abstract

Multimodal container terminals (MMCTs) are very complex and consequently require synchronization and balancing of container transfers at each node. The problem being investigated is the minimization of ship delays at the port by considering handling and travelling time of containers from the time the ship arrives at port until all the containers from that ship leave the port. When dealing with export containers, the problem would be that of the handling and travelling time of the containers from when they first arrive at the port until the ship carrying the containers departs from the port. Owing to the dynamic nature of the environment, a large number of timely decisions have been reviewed in accordance with the changing conditions of the MMCTs. The model has been run and tested with a small-size problem using CPLEX. A more realistic model is extremely difficult to solve and is in fact proven to be computationally intractable (NP-hard). Metaheuristics have been developed to deal with the intractability so that near-optimal solutions could be obtained in reasonable time.

Keywords

simulation heuristics container multimodal 

References

  1. Glover F (1990). Tabu search: a tutorial. Interfaces 20(4): 74–79.CrossRefGoogle Scholar
  2. Glover F (1993). A user's guide to tabu search. Ann Opns Res 41: 3–28.CrossRefGoogle Scholar
  3. Kim KH and Kim HB (2002). The optimal sizing of the storage space and handling facilities for import containers. Transport Res B: Meth 36: 821–835.CrossRefGoogle Scholar
  4. Kim KH and Kim KY (1999). Routing straddle carriers for the loading operation of containers using a beam search algorithm. Comput Ind Eng 36: 109–136.CrossRefGoogle Scholar
  5. Kondratowicz LJ (1990). Simulation methodology for intermodal freight transportation terminals. Simulation 55: 49–58.CrossRefGoogle Scholar
  6. Kozan E (1997a). Increasing the operational efficiency of container terminals in Australia. J Opl Res Soc 48: 151–161.CrossRefGoogle Scholar
  7. Kozan E (1997b). Comparison of analytical and simulation planning models of seaport container terminals. Transport Plan Technol 20: 235–248.CrossRefGoogle Scholar
  8. Kozan E (2000). Optimising container transfers at multimodal terminals. Math Comput Model 31: 235–244.CrossRefGoogle Scholar
  9. Kozan E and Preston P (1999). Genetic algorithms to schedule container transfers at multimodal terminals. Int Trans Opl Res 6: 311–329.CrossRefGoogle Scholar
  10. Pinedo ML (2002). Scheduling: Theory, Algorithms and Systems. Prentice-Hall: Englewood Cliffs, NJ.Google Scholar
  11. Preston P and Kozan E (2001a). An approach to determine storage locations of containers at seaport terminals. Comput Opns Res 28: 983–995.CrossRefGoogle Scholar
  12. Preston P and Kozan E (2001b). A tabu search technique applied to scheduling container transfers. Transport Plan Technol 24: 135–154.CrossRefGoogle Scholar
  13. Semet F and Taillard E (1993). Solving real life vehicle routing problems efficiently using tabu search. Ann Opns Res 41: 469–488.CrossRefGoogle Scholar
  14. Steenken D, Voß S and Stahlbock R (2004). Container terminal operation and operations research—a classification and literature review. OR Spectrum 26: 3–49.CrossRefGoogle Scholar
  15. Taleb-Ibrahimi M, de Castilho B and Daganzo CF (1993). Storage space vs handling work in container terminals. Transport Res B 27B1: 13–32.CrossRefGoogle Scholar
  16. Vis I and de Koster R (2003). Transshipment of containers at a container terminal: an overview. Eur J Op Res 147: 1–16.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan Ltd 2006

Authors and Affiliations

  1. 1.Queensland University of TechnologyBrisbaneAustralia

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