Skip to main content
SpringerLink
Log in

Container Vessel Stowage Planning System Using Genetic Algorithm

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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_27

    Chapter  Google Scholar 

  3. Ambrosino, D., Sciomachen, A., Tanfani, E.: A decomposition heuristics for the container ship stowage problem. J. Heuristics 12(3), 211–233 (2006)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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). http://dl.acm.org/citation.cfm?id=647138.717368

  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 2007

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  12. Kang, J.-G., Kim, Y.-D.: Stowage planning in maritime container transportation. J. Oper. Res. Soc. 53(4), 415–426 (2002). http://www.jstor.org/stable/822825

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. 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_90

    Chapter  Google Scholar 

  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. Pacino, D.: Fast generation of container vessel stowage plans. Ph.D. thesis, IT University of Copenhagen (2012)

    Google Scholar 

  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_22

    Chapter  Google Scholar 

  19. Rodrigo, J.: Container ship safety, maritime Law (UPC). http://upcommons.upc.edu/e-prints/handle/2117/3051

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering, Braude College of Engineering, Karmiel, Israel

    Miri Weiss Cohen, Adi Dahan & Izzik Kaspi

  2. Institute of Computer Science, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil

    Vitor Nazário Coelho

  3. Brazil Grupo da Causa Humana, Ouro Preto, Minas Gerais, Brazil

    Vitor Nazário Coelho

Authors
  1. Miri Weiss Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vitor Nazário Coelho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adi Dahan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Izzik Kaspi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miri Weiss Cohen .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Cohen, M.W., Coelho, V.N., Dahan, A., Kaspi, I. (2017). Container Vessel Stowage Planning System Using Genetic Algorithm. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55849-3_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55848-6

  • Online ISBN: 978-3-319-55849-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation