Artificial Intelligence Techniques for the Berth Allocation and Container Stacking Problems in Container Terminals

  • Miguel A. Salido
  • Mario Rodriguez-Molins
  • Federico Barber
Conference paper


The Container Stacking Problem and the Berth Allocation Problem are two important problems in maritime container terminal’s management which are clearly related. Terminal operators normally demand all containers to be loaded into an incoming vessel should be ready and easily accessible in the terminal before vessel’s arrival. Similarly, customers (i.e., vessel owners) expect prompt berthing of their vessels upon arrival. In this paper, we present an artificial intelligence based-integrated system to relate these problems. Firstly, we develop a metaheuristic algorithm for berth allocation which generates an optimized order of vessel to be served according to existing berth constraints. Secondly, we develop a domain-oriented heuristic planner for calculating the number of reshuffles needed to allocate containers in the appropriate place for a given berth ordering of vessels. By combining these optimized solutions, terminal operators can be assisted to decide the most appropriated solution in each particular case.


Greedy Randomize Adaptive Search Procedure Container Terminal Quay Crane Terminal Operator Complete Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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This work has been partially supported by the research projects TIN2007-67943-C02-01 (MEC, Spain-FEDER), and P19/08 (M. Fomento, Spain-FEDER).


  1. 1.
    Henesey, L. (2006). Overview of Transshipment Operations and Simulation. In: MedTrade conference, Malta, April. pp. 6–7.Google Scholar
  2. 2.
    Stahlbock, R. and Voß, S. (2008). Operations research at container terminals: a literature update. OR Spectrum 30(1), 1–52.MATHCrossRefGoogle Scholar
  3. 3.
    Giallombardo, G., Moccia, L., Salani, M., and Vacca, I. (2010). Modeling and solving the tactical berth allocation problem. Transportation Research Part B: Methodological 44(2), 232–245.CrossRefGoogle Scholar
  4. 4.
    Yusin, L., and Hsu, N.Y. (2007). An optimization model for the container pre-marshalling problem. Computers & Operations Research 34(11), 3295–3313.MATHCrossRefGoogle Scholar
  5. 5.
    Park, K., T. Park and K.R. Ryu (2009). Planning for remarshaling in an automated container terminal using cooperative coevolutionary algorithms. In: ACM symposium on Applied Computing. ACM. pp. 1098–1105.Google Scholar
  6. 6.
    Kim, K.H. and Hong G.P. (2006). A heuristic rule for relocating blocks. Computers & Operations Research 33(4), 940–954.MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Winograd T. (1971). Procedures as a representation for data in a computer program for understanding natural language. MIT. Cent. Space Res.Google Scholar
  8. 8.
    Salido, M., Sapena, O and Barber F. (2009). The Container Stacking Problem: an Artificial Intelligence Planning-Based Approach. In Proc. of The Int. Workshop on Harbour, Maritime and Multimodal Logistics Modelling and Simulation HMS’2009. pp:127-131.Google Scholar
  9. 9.
    Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D., and Wilkins, D. (1998). PDDL - the planning domain definition language. AIPS-98 Planning Committee.Google Scholar
  10. 10.
    Hoffmann, J. (2003). The metric-FF planning system: translating “ignoring delete lists” to numeric state variables. J. Artif. Int. Res. 20(1), 291–341.MATHGoogle Scholar
  11. 11.
    Rodriguez, M, Salido, M., Barber F. (2009a). Domain-Dependent Planning Heuristics for Locating Containers in Maritime Terminals. Trends in Applied Intelligent Systems. IEA/AIE 2010, LNAI 6096, pp. 742–751.Google Scholar
  12. 12.
    Theofanis, S., Boile, M. and Golias M.M. (2009). Container terminal berth planning. Transportation Research Record: Journal of the Transportation Research Board 2100(-1), 22–28.CrossRefGoogle Scholar
  13. 13.
    Lai, KK and Shih, K. (1992). A study of container berth allocation. Journal of Advanced Transportation 26(1), 45–60.CrossRefGoogle Scholar
  14. 14.
    Guan, Y. and Cheung, R.K. (2004). The berth allocation problem: models and solution methods. OR Spectrum 26(1), 75–92.MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Cordeau, J.F., Laporte, G., Legato, P. and Moccia, L. (2005). Models and tabu search heuristics for the berth-allocation problem. Transportation science 39(4), 526–538.CrossRefGoogle Scholar
  16. 16.
    Cheong, C.Y., Tan, K.C. and Liu, D.K. (2009). Solving the berth allocation problem with service priority via multi- objective optimization. In: Computational Intell. in Scheduling, 2009. CI-Sched ’09. IEEE Symposium on. pp. 95 –102.Google Scholar
  17. 17.
    Feo, T.A. and Resende, M.G.C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization 6(2), 109–133.MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Instituto de Automatica e Informatica industrial, Universidad Politecnica de ValenciaValenciaSpain

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