Constructive Algorithm for a Benchmark in Ship Stowage Planning

  • Laura Cruz-ReyesEmail author
  • Paula Hernández H.
  • Patricia Melin
  • Héctor J. Fraire H.
  • Julio Mar O.
Part of the Studies in Computational Intelligence book series (SCI, volume 451)


The efficiency of a maritime container terminal mainly depends on the process of handling containers, especially during the ships loading process. A good stowage planning facilitates these processes. This paper deals with the containership stowage problem, referred to as the Master Bay Plan Problem (MBPP). It is a NP-hard minimization problem whose goal is to find optimal plans for stowing containers into a containership with a low containership operation cost, subject to a set of structural and operational restrictions. For MBPP, data are not available for confidentiality reasons. The lack of a performance evaluation benchmark of solution algorithms for MBPP raises the need for a generation of instances. Due to this limitation, we present a generation scheme of instances for the MBPP, which is based random generation according on selected sets of parameters. The parameters are variable within certain ranges to characterize the vessel and containers; the ranges are real-life values taken from the literature. A constructive loading heuristic for stowing containers into a containership is proposed in this paper to have reference solutions. An instance set, its known-best solutions and the generator are available on-line.


Container Terminal Constructive Algorithm Quay Crane Weight Constraint Container Vessel 
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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  1. 1.
    Amil, C.: Historia de los puertos. Instituto Universitario de Estudios Marítimos (December 9, 2004),
  2. 2.
    Rúa, C.: Los puertos en el transporte marítimo. Instituto de organización y control de sistemas industriales. Universidad de Cataluña, Barcelona (2006)Google Scholar
  3. 3.
    Steenken, D., Voss, S., Stahlbock, R.: Container terminal operation and operations research – A classification and literature review. OR Spectrum 26(1), 3–49 (2004)Google Scholar
  4. 4.
    Vacca, I., Salani, M., Bierlaire, M.: Optimization of operations in container terminals: hierarchical vs integrated approaches. European Journal of Operational Research (2010)Google Scholar
  5. 5.
    Delgado, A., Jensen, R.M., Janstrup, K., Rose, T.H., Andersen, K.H.: A Constraint Programming Model for Fast Optimal Stowage of Container Vessel Bays. European Journal of Operational Research (2012)Google Scholar
  6. 6.
    Meisel, F.: Seaside Operations Planning in Container Terminals. Contributions to Management Science. Physica Verlag Heidelberg (2009)Google Scholar
  7. 7.
    Ambrosino, D., Sciomachen, A., Tanfani, E.: Stowing a Containership: The Master Bay Plan problem. Transportation Research Part A: Policy and Practice 38, 81–99 (2004)CrossRefGoogle Scholar
  8. 8.
    Fan, L., Low, M.Y.H., Ying, H.S., Jing, H.W., Min, Z., Aye, W.C.: Stowage Planning of Large Containership with Tradeoff between Crane Work-load Balance and Ship Stability. In: Proceedings of the International Multi-Conference of Engineers and Computer Scientists, vol. 3 (2010)Google Scholar
  9. 9.
    Zeng, M., Low, M.Y.H., Hsu, W.J., Huang, S.Y., Liu, F., Win, C.A.: Automated stowage planning for large containerships with improved safety and stability. In: Proceedings of the 2010 Winter Simulation Conference, WSC 2010 (2010)Google Scholar
  10. 10.
    Avriel, M., Penn, M., Shpirer, N.: Container ship Stowage Problem: Complexity and Connection to the Coloring of Circle Graphs. Discrete Applied Mathematics 103(1), 271–279 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Ambrosino, D., Sciomachen, A., Tanfani, E.: A decomposition heuristics for the container ship stowage problem. Journal of Heuristics 12, 211–233 (2006)zbMATHCrossRefGoogle Scholar
  12. 12.
    Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: An experimental comparison of different heuristics for the master bay plan problem. Experimental Algorithms, 314–325 (2010)Google Scholar
  13. 13.
    Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: A new three-step heuristic for the master bay plan problem. Maritime Economics and Logistics 11(1), 98–120 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Laura Cruz-Reyes
    • 1
    Email author
  • Paula Hernández H.
    • 1
  • Patricia Melin
    • 2
  • Héctor J. Fraire H.
    • 1
  • Julio Mar O.
    • 3
  1. 1.Instituto Tecnológico de Ciudad MaderoCiudad MaderoMéxico
  2. 2.Tijuana Institute of TechnologyTijuanaMexico
  3. 3.Universidad Autónoma de TamaulipasCiudad VictoriaMéxico

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