A Loading Procedure for the Containership Stowage Problem

  • Laura Cruz-ReyesEmail author
  • Paula Hernández Hernández
  • Patricia Melin
  • Héctor Joaquín Fraire Huacuja
  • Julio Mar-Ortiz
  • Héctor José Puga Soberanes
  • Juan Javier González Barbosa
Part of the Studies in Computational Intelligence book series (SCI, volume 547)


This chapter deals with the containership stowage problem. It is an NP-hard combinatorial optimization whose goal is to find optimal plans for stowing containers into a containership with low operational costs, subject to a set of structural and operational constraints. In order to optimize a stowage planning, like in the literature, we have developed an approach that decomposes the problem hierarchically. This approach divides the problem into two phases: the first one consists of generating a relaxed initial solution, and the second phase is intended to make this solution feasible. In this chapter, we focus on the first phase of this approach, and a new loading procedure to generate an initial solution is proposed. This procedure produces solutions in short running time, so that, it could be applied to solve real instances.


Linear Programming Model Container Terminal Quay Crane Weight Constraint Destination Port 
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.



This work was partially financed by CONACYT, PROMEP and DGEST. We also thank Gurobi for allowing us to use their optimization engine.


  1. 1.
    Ambrosino, D., Sciomachen, A., Tanfani, E.: Stowing a containership: the master bay plan problem. Transp. Res. Part A: Policy Pract. 38, 81–99 (2004)CrossRefGoogle Scholar
  2. 2.
    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. Eur. J. Oper. Res. 220, 251–261 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: A new three-stepheuristic for the master bay plan problem. Marit. Econ. Logistics 11, 98–120 (2009)CrossRefGoogle Scholar
  4. 4.
    Hernández, P.H., Cruz-Reyes, L., Melin, P., Mar-Ortiz, J., Huacuja, H.J.F., Soberanes, H.J.P., Barbosa, J.J.G.: An ant colony algorithm for improving ship stability in the containership stowage problem. In: Castro, F., Gelbukh, A., González, M. (eds.) Advances in Soft Computing and Its Applications. Lecture Notes in Computer Science, vol. 8266, pp. 93–104. Springer, Berlin (2013)CrossRefGoogle Scholar
  5. 5.
    Ambrosino, D., Sciomachen, A., Tanfani, E.: A decomposition heuristics for the container ship stowage problem. J. Heuristics 12, 211–233 (2006)CrossRefzbMATHGoogle Scholar
  6. 6.
    Avriel, M., Penn, M., Shpirer, N.: Container ship Stowage problem: complexity and connection to the coloring of circle graphs. Discrete Appl. Math. 103(1), 271–279 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Cruz-Reyes, L., Hernández, P., Melin, P., et al.: Constructive algorithm for a benchmark in ship stowage planning. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems, pp. 393–408. Springer, Berlin (2013)Google Scholar
  8. 8.
    Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., Valdez, M.: Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst. Appl. 40(8), 3196–3206 (2013)CrossRefGoogle Scholar
  9. 9.
    Montiel, O., Camacho, J., Sepúlveda, R., Castillo, O.: Fuzzy system to control the movement of a wheeled mobile robot. Soft Comput. Intell. Control Mobile Robot. 318, 445–463 (2011)Google Scholar
  10. 10.
    Montiel, O., Sepulveda, R., Melin, P., Castillo, O., Porta, M. A., Meza, I.M.: Performance of a simple tuned fuzzy controller and a PID controller on a DC motor. In: FOCI 2007, pp. 531–537 (2007)Google Scholar
  11. 11.
    Sombra A., Valdez F., Melin P., Castillo O.: A new gravitational search algorithm using fuzzy logic to parameter adaptation. In: IEEE Congress on Evolutionary Computation 2013, pp. 1068–1074 (2013)Google Scholar
  12. 12.
    Valdez F., Melin P., Castillo O.: Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making. In: Proceedings of the IEEE International Conference on Fuzzy Systems, 2009, pp. 2114–2119 (2009)Google Scholar
  13. 13.
    Valdez F., Melin P., Castillo O.: Fuzzy logic for parameter tuning in evolutionary computation and bio-inspired methods. In: MICAI (2) 2010, pp. 465–474 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Laura Cruz-Reyes
    • 1
    Email author
  • Paula Hernández Hernández
    • 1
  • Patricia Melin
    • 2
  • Héctor Joaquín Fraire Huacuja
    • 1
  • Julio Mar-Ortiz
    • 3
  • Héctor José Puga Soberanes
    • 4
  • Juan Javier González Barbosa
    • 1
  1. 1.Instituto Tecnológico de Ciudad MaderoCiudad MaderoMéxico
  2. 2.Tijuana Institute of TechnologyTijuanaMéxico
  3. 3.Universidad Autónoma de TamaulipasTampicoMéxico
  4. 4.Instituto Tecnológico de León LeónMéxico

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