Container Vessel Stowage Planning System Using Genetic Algorithm

  • Miri Weiss Cohen
  • Vitor Nazário Coelho
  • Adi Dahan
  • Izzik Kaspi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)


This paper deals with the container stowage planning problem, an important and a complex problem in maritime logistic optimization. The variant tackled in this work involves several constraints, inspired by real-life problems and application found in the literature. Given the complexity of the problem, which belongs to the class of \(\mathcal {NP}\)-hard problems, a novel evolutionary metaheuristic algorithm is developed and designed. Considering the ability and flexibility of Genetic Algorithm (GA). The approach is based on a two-phase procedure, one for master planning and the other for allocation of the containers into slots. GA parameters are analyzed to achieve practical and best results. The system offers stowage allocation solutions for both phases, thus offering flexibility for a wide variety of vessels and route combinations.


Container vessel stowage planning Genetic Algorithm Metaheuristic Constraint optimization 


  1. 1.
    Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: A new three-step heuristic for the master bay plan problem. Marit. Econ. Logistics 11(1), 98–120 (2009)CrossRefGoogle Scholar
  2. 2.
    Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: An experimental comparison of different heuristics for the master bay plan problem. In: Festa, P. (ed.) SEA 2010. LNCS, vol. 6049, pp. 314–325. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13193-6_27CrossRefGoogle Scholar
  3. 3.
    Ambrosino, D., Sciomachen, A., Tanfani, E.: A decomposition heuristics for the container ship stowage problem. J. Heuristics 12(3), 211–233 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Avriel, M., Penn, M., Shpirer, N.: Container ship stowage problem: complexity and connection to the coloring of circle graphs. Discrete Appl. Math. 103(1–3), 271–279 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Avriel, M., Penn, M., Shpirer, N., Witteboon, S.: Stowage planning for container ships to reduce the number of shifts. Ann. Oper. Res. 76, 55–71 (1998)CrossRefzbMATHGoogle Scholar
  6. 6.
    Botter, R.C., Brinati, M.A.: Stowage container planning: a model for getting an optimal solution. In: Proceedings of the IFIP TC5/WG5.6 Seventh International Conference on Computer Applications in the Automation of Shipyard Operation and Ship Design, vol. 7, pp. 217–229. North-Holland Publishing Co. (1992).
  7. 7.
    Carrano, E., Fonseca, C., Takahashi, R., Pimenta, L., Neto, O.: A preliminary comparison of tree encoding schemes for evolutionary algorithms. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1969–1974. ISIC, October 2007Google Scholar
  8. 8.
    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(1), 251–261 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Ding, D., Chou, M.C.: Stowage planning for container ships: a heuristic algorithm to reduce the number of shifts. Eur. J. Oper. Res. 246(1), 242–249 (2015)CrossRefzbMATHGoogle Scholar
  10. 10.
    Dubrovsky, O., Levitin, G., Penn, M.: A genetic algorithm with a compact solution encoding for the container ship stowage problem. J. Heuristics 8(6), 585–599 (2002)CrossRefGoogle Scholar
  11. 11.
    Imai, A., Sasaki, K., Nishimura, E., Papadimitriou, S.: Multi-objective simultaneous stowage and load planning for a container ship with container rehandle in yard stacks. Eur. J. Oper. Res. 171(2), 373–389 (2006)CrossRefzbMATHGoogle Scholar
  12. 12.
    Kang, J.-G., Kim, Y.-D.: Stowage planning in maritime container transportation. J. Oper. Res. Soc. 53(4), 415–426 (2002). Scholar
  13. 13.
    Jensen, R.M., Leknes, E., Bebbington, T.: Fast interactive decision support for modifying stowage plans using binary decision diagrams. In: International Multiconference of Engineers and Computer Scientists (2012)Google Scholar
  14. 14.
    Kumar, R., Gopal, G., Kumar, R.: Novel crossover operator for genetic algorithm for permutation problems. Int. J. Soft Comput. Eng. (IJSCE) 3(2), 252–258 (2013)Google Scholar
  15. 15.
    Li, F., Tian, C., Cao, R., Ding, W.: An integer linear programming for container stowage problem. In: Bubak, M., Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008. LNCS, vol. 5101, pp. 853–862. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69384-0_90CrossRefGoogle Scholar
  16. 16.
    Malhotra, R., Singh, N., Singh, Y.: Genetic algorithms: concepts, design for optimization of process controllers. Comput. Inf. Sci. 4(2), 39–59 (2011)Google Scholar
  17. 17.
    Pacino, D.: Fast generation of container vessel stowage plans. Ph.D. thesis, IT University of Copenhagen (2012)Google Scholar
  18. 18.
    Pacino, D., Delgado, A., Jensen, R.M., Bebbington, T.: Fast generation of near-optimal plans for eco-efficient stowage of large container vessels. In: Böse, J.W., Hu, H., Jahn, C., Shi, X., Stahlbock, R., Voß, S. (eds.) ICCL 2011. LNCS, vol. 6971, pp. 286–301. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24264-9_22CrossRefGoogle Scholar
  19. 19.
    Rodrigo, J.: Container ship safety, maritime Law (UPC).
  20. 20.
    Sciomachen, A., Tanfani, E.: The master bay plan problem: a solution method based on its connection to the three-dimensional bin packing problem. IMA J. Manage. Math. 14(3), 251–269 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Sciomachen, A., Tanfani, E.: A 3D-BPP approach for optimising stowage plans and terminal productivity. Eur. J. Oper. Res. 183(3), 1433–1446 (2007)CrossRefzbMATHGoogle Scholar
  22. 22.
    Wei-ying, Z., Yan, L., Zhuo-shang, J.: Model and algorithm for container ship stowage planning based on bin-packing problem. J. Mar. Sci. Appl. 4(3), 30–36 (2005)CrossRefGoogle Scholar
  23. 23.
    Wilson, I., Roach, P., Ware, J.: Container stowage pre-planning: using search to generate solutions, a case study. Knowl. Based Syst. 14(3–4), 137–145 (2001)CrossRefGoogle Scholar
  24. 24.
    Yang, J.H., Kim, K.H.: A grouped storage method for minimizing relocations in block stacking systems. J. Intell. Manuf. 17(4), 453–463 (2006)CrossRefGoogle Scholar
  25. 25.
    Yoke, M., Low, H., Xiao, X., Liu, F., Huang, S.Y., Hsu, W.J., Li, Z.: An automated stowage planning system for large container ships. In: Proceedings of the 4th Virtual International Conference on Intelligent Production Machines and Systems (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Miri Weiss Cohen
    • 1
  • Vitor Nazário Coelho
    • 2
    • 3
  • Adi Dahan
    • 1
  • Izzik Kaspi
    • 1
  1. 1.Department of Software EngineeringBraude College of EngineeringKarmielIsrael
  2. 2.Institute of Computer ScienceUniversidade Federal FluminenseNiteróiBrazil
  3. 3.Brazil Grupo da Causa HumanaOuro PretoBrazil

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