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Multi-objective Optimization for Liner Shipping Fleet Repositioning

  • Kevin TierneyEmail author
  • Joshua Handali
  • Christian Grimme
  • Heike Trautmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)

Abstract

The liner shipping fleet repositioning problem (LSFRP) is a central optimization problem within the container shipping industry. Several approaches exist for solving this problem using exact and heuristic techniques, however all of them use a single objective function for determining an optimal solution. We propose a multi-objective approach based on a simulated annealing heuristic so that repositioning coordinators can better balance profit making with cost-savings and environmental sustainability. As the first multi-objective approach in the area of liner shipping routing, we show that giving more options to decision makers need not be costly. Indeed, our approach requires no extra runtime than a weighted objective heuristic and provides a rich set of solutions along the Pareto front.

Keywords

Pareto Front Empty Container Liner Shipping Goal Service Multiobjective Approach 
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.

Notes

Acknowledgements

Christian Grimme and Heike Trautmann acknowledge support from the European Center for Information Systems (ERCIS).

References

  1. 1.
    Becker, M., Tierney, K.: A hybrid reactive tabu search for liner shipping fleet repositioning. In: Corman, F., Voß, S., Negenborn, R.R. (eds.) ICCL 2015. LNCS, vol. 9335, pp. 123–138. Springer, Cham (2015). doi: 10.1007/978-3-319-24264-4_9 CrossRefGoogle Scholar
  2. 2.
    Blasco, X., Herrero, J., Sanchis, J., Martínez, M.: A new graphical visualization of n-dimensional pareto front for decision-making in multiobjective optimization. Inf. Sci. 178(20), 3908–3924 (2008)CrossRefzbMATHGoogle Scholar
  3. 3.
    Brouer, B., Alvarez, J., Plum, C., Pisinger, D., Sigurd, M.: A base integer programming model and benchmark suite for liner-shipping network design. Transp. Sci. 48(2), 281–312 (2013)CrossRefGoogle Scholar
  4. 4.
    Christiansen, M., Fagerholt, K., Nygreen, B., Ronen, D.: Ship routing and scheduling in the new millennium. Eur. J. Oper. Res. 228(3), 467–483 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Fagerholt, K.: A computer-based decision support system for vessel fleet schedulingexperience and future research. Decis. Support Syst. 37(1), 35–47 (2004)CrossRefGoogle Scholar
  6. 6.
    Guericke, S., Tierney, K.: Liner shipping cargo allocation with service levels and speed optimization. Transp. Res. Part E Logistics Transp. Rev. 84, 40–60 (2015)CrossRefGoogle Scholar
  7. 7.
    INTERSCHALT Maritime Systems AG: StowMan[s]: Efficient stowage planning for higher cargo intake. A case study, November 2015. http://www.interschalt.com/fileadmin/user_upload/StowManS_Case_Study.pdf. Accessed 26 Sep 2016
  8. 8.
    Mansouri, S.A., Lee, H., Aluko, O.: Multi-objective decision support to enhance environmental sustainability in maritime shipping: a review and future directions. Transp. Res. Part E Logistics Transp. Rev. 78, 3–18 (2015)CrossRefGoogle Scholar
  9. 9.
    Meyer, J., Stahlbock, R., Voß, S.: Slow steaming in container shipping. In: 45th Hawaii International Conference on System Science (HICSS 2012), pp. 1306–1314. IEEE (2012)Google Scholar
  10. 10.
    Müller, D., Tierney, K.: Decision support and data visualization for liner shipping fleet repositioning. Inf. Technol. Manage. 1–19 (2016)Google Scholar
  11. 11.
    Perakis, A., Jaramillo, D.: Fleet deployment optimization for liner shipping Part 1. Background, problem formulation and solution approaches. Marit. Policy Manage. 18(3), 183–200 (1991)CrossRefGoogle Scholar
  12. 12.
    Tierney, K., Askelsdóttir, B., Jensen, R., Pisinger, D.: Solving the liner shipping fleet repositioning problem with cargo flows. Transp. Sci. 49(3), 652–674 (2014)CrossRefGoogle Scholar
  13. 13.
    Tierney, K., Coles, A., Coles, A., Kroer, C., Britt, A., Jensen, R.: Automated planning for liner shipping fleet repositioning. In: McCluskey, L., Williams, B., Silva, J., Bonet, B. (eds.) Proceedings of the 22nd International Conference on Automated Planning and Scheduling, pp. 279–287 (2012)Google Scholar
  14. 14.
    Tierney, K., Jensen, R.M.: The liner shipping fleet repositioning problem with cargo flows. In: Hu, H., Shi, X., Stahlbock, R., Voß, S. (eds.) ICCL 2012. LNCS, vol. 7555, pp. 1–16. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33587-7_1 CrossRefGoogle Scholar
  15. 15.
    Tierney, K., Jensen, R.M.: A node flow model for the inflexible visitation liner shipping fleet repositioning problem with cargo flows. In: Pacino, D., Voß, S., Jensen, R.M. (eds.) ICCL 2013. LNCS, vol. 8197, pp. 18–34. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41019-2_2 CrossRefGoogle Scholar
  16. 16.
    Tierney, K., Pacino, D., Jensen, R.: On the complexity of container stowage planning problems. Discrete Appl. Math. 169, 225–230 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Tierney, K.: Optimizing Liner Shipping Fleet Repositioning Plans. Springer, Cham (2015)CrossRefzbMATHGoogle Scholar
  18. 18.
    United Nations Conference on Trade and Development (UNCTAD): Review of maritime transport (2015)Google Scholar
  19. 19.
    Wong, E., Lau, H., Mak, K.: Immunity-based evolutionary algorithm for optimal global container repositioning in liner shipping. OR Spectr. 32(3), 739–763 (2010)CrossRefzbMATHGoogle Scholar
  20. 20.
    Wong, E., Yeung, H., Lau, H.: Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning. Eng. Appl. Artif. Intell. 22(6), 842–854 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kevin Tierney
    • 1
    Email author
  • Joshua Handali
    • 2
  • Christian Grimme
    • 2
  • Heike Trautmann
    • 2
  1. 1.Decision Support and Operations Research LabUniversity of PaderbornPaderbornGermany
  2. 2.Information Systems and Statistics GroupUniversity of MünsterMünsterGermany

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