Minimizing overstowage in master bay plans of large container ships
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This study proposes a mathematical model that minimizes overstowage in class-based master bay plans for large container ships. With the advantage of an underlying multi-commodity network structure, solutions are obtained within reasonable computation time in the case of 14,000-TEU ships. In addition, changeable bay configuration patterns due to different container sizes—an important but rarely discussed issue—are efficiently addressed by adding specially designed arcs and side constraints. Our model can support container stowage planners by providing a flexible and efficient approach for allocating ship slots to containers of different lengths when preparing class-based master bay plans for large container ships.
KeywordsContainers Liner shipping Stowage planning Multi-commodity networks
The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this work under Contract No. NSC 101-2410-H-019-023.
- Alvarez, J. 2006. A heuristic for vessel planning in a reach stacker terminal. Journal of Maritime Research III (1): 3–16.Google Scholar
- Ambrosino, D., M. Paolucci, and A. Sciomachen. 2018. Shipping liner company stowage plans: An optimization approach. In Advanced concepts, methodologies and technologies for transportation and logistics, ed. J. Żak, Y. Hadas, and R. Rossi, 405–420. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-57105-8_20.CrossRefGoogle Scholar
- Aslidis, A.H. 1989. Combinatorial algorithms for stacking problems. Ph.D. Dissertation, Massachusetts Institute of Technology.Google Scholar
- Iris, C., and D. Pacino. 2015. A survey on the ship loading problem. In 6th International conference on computational logistics (ICCL 2015), vol. 9335, ed. F. Corman, S. Voß, and R.R. Negenborn, 238–251., Lecture notes in computer science Cham: Springer International Publishing.Google Scholar
- Kim, K., and H. Kim. 1999a. Segregating space allocation models for container inventories in port container terminals. International Journal of Production Economics 59(1–3): 415–423.Google Scholar
- Malawski, M., K. Figiela, M. Bubak, E. Deelman, and J. Nabrzyski. 2014. Cost optimization of execution of multi-level deadline-constrained scientific workflows on clouds. In Parallel processing and applied mathematics (PPAM 2013), ed. R. Wyrzykowski, J. Dongarra, K. Karczewski, and J. Waśniewski., Lecture notes in computer science Berlin: Springer.Google Scholar
- Pacino, D., A. Delgado, R.M. Jensen, and T. Bebbington. 2012. An accurate model for seaworthy container vessel stowage planning with ballast tanks. In Computational logistics, vol. 7555, ed. H. Hu, X. Shi, R. Stahlbock, and S. Voß, 17–32., Lecture notes in computer science Cham: Springer International Publishing.CrossRefGoogle Scholar
- Webster, W.C., and P. Van Dyke. 1970. Container loading. A container allocation model: I—introduction background, II—strategy, conclusions. In Proceedings of computer-aided ship design engineering summer conference, University of Michigan.Google Scholar
- Xiao, X., M.Y.H. Low, F. Liu, S.Y. Huang, W.J. Hsu, and Z.P. Li. 2009. An efficient block-based heuristic method for stowage planning of large containerships with crane split consideration. In Proceedings of the International Conference on Harbor, Maritime & Multimodal Logistics Modeling and Simulation, pp. 93–99.Google Scholar