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A shipping line stowage-planning procedure in the presence of hazardous containers

  • Daniela Ambrosino
  • Anna Sciomachen
Original Article
  • 18 Downloads

Abstract

This work addresses the stowage-planning problem for containerships, known as the Master Bay Plan problem (MBPP), in the presence of hazardous containers. A novel procedure, based on the principles included in the International Maritime Dangerous Goods (IMDG) Code for stowing containers in liner services is presented. Further, shipping alliances are considered. Our aim is to assist the shipping line coordinator (SLC) to optimize the available space assigned to each alliance member. This is possible thanks to the proposed procedure that finds stowage solutions for ships with different structures, capacity and available sections for hazardous containers, and for companies having different stowage strategies. Our procedure can be implemented in a tool, able to verify the stowage constraints and the segregation rules in case of hazardous cargo. Two simple real-life multi-port stowage plans involving hazardous containers are presented and analysed to illustrate the proposed procedure.

Keywords

Stowage plans Hazardous containers Liner shipping Segregation tables International Maritime Dangerous Goods Shipping alliances 

Notes

Acknowledgements

The authors wish to thank the reviewers for their work and their comments and suggestions that enabled us to improve the manuscript. Special thanks to the editor, Hercules Haralambides, for his precious additional suggestions.

References

  1. Ambrosino, D., A. Sciomachen, and E. Tanfani. 2004. Stowing a containership: the master bay plan problem. Transportation Research Part A 38: 81–99.CrossRefGoogle Scholar
  2. Ambrosino, D., D. Anghinolfi, M. Paolucci, and A. Sciomachen. 2009. A new three-step heuristic for the Master Bay Plan Problem. Maritime Economics & Logistics 11: 98–120.CrossRefGoogle Scholar
  3. Ambrosino, D., M. Paolucci, and A. Sciomachen. 2015. Experimental evaluation of mixed integer programming models for the multi-port master bay plan problem. Flexible Service & Manufacturing Journal 27: 263–284.CrossRefGoogle Scholar
  4. Ambrosino, D., and A. Sciomachen. 2015. Using a bin packing approach for stowing hazardous containers into containerships. In Optimized packings with applications, ed. G. Fasano, and J.D. Pintér, 1–17. New York: Springer.Google Scholar
  5. Ambrosino, D., M. Paolucci, and A. Sciomachen. 2017. Computational evaluation of a MIP model for a multi-port stowage planning problem. Soft Computing 21 (7): 1753–1763.CrossRefGoogle Scholar
  6. Avriel, M., M. Penn, and N. Shpirer. 2000. Container ship stowage problem: complexity and connection to the colouring of circle graphs. Discrete Applied Mathematics 103: 271–279.CrossRefGoogle Scholar
  7. Carlo, H.J., I.F.A. Vis, and K.J. Roodbergen. 2013. Storage yard operations in container terminals: literature overview, trends, and research directions. European Journal of Operational Research 235: 412–430.CrossRefGoogle Scholar
  8. Ding, D., and M.C. Chou. 2015. Stowage planning for container ships: a heuristic algorithm to reduce the number of shifts. European Journal of Operational Research 246: 242–249.CrossRefGoogle Scholar
  9. Komini, L. 2014. The rise and dominance of Shipping Alliances in the Liner industry, 1–18. Cardiff: Cardiff Business School.Google Scholar
  10. Imai, A., E. Nishimura, and S. Papadimitriou. 2013. Marine container terminal configurations for efficient handling of mega-containerships. Transportation Research Part E 49 (1): 141–158.CrossRefGoogle Scholar
  11. Imai, A., K. Sasaki, E. Nishimura, and S. Papadimitriou. 2006. Multi-objective simultaneous stowage and load planning for a container ship with container rehandles in yard stacks. European Journal of Operational Research 171: 373–389.CrossRefGoogle Scholar
  12. IMO, International Convention for Safe Containers (CSC), 1972.Google Scholar
  13. IMO, International Convention for the Prevention of Pollution from Ships, (MARPOL 73/78), adopted in 1973, modified by Protocol 1978.Google Scholar
  14. IMO, International Convention for the Safety of Life at Sea (SOLAS), 1974, Chapter VII, Chapter II-2.Google Scholar
  15. IMO, International Maritime Dangerous Goods (IMDG) Code. (2014). Edition incorporating Amendment 37-14.Google Scholar
  16. Monaco, M.F., M. Sammarra, and G. Sorrentino. 2014. The terminal-oriented ship stowage planning problem. European Journal of Operational Research 239: 256–265.CrossRefGoogle Scholar
  17. Pacino, D., A. Delgado, R.M. Jensen, and T. Bebbington. 2011. Fast generation of near-optimal plans for eco- efficient stowage of large container vessels. In Computational logistics. Lecture notes in computer science, ed. J. Bse, H. Hu, C. Jahn, X. Shi, R. Stahlbock, and S. Voss, 286–301. Antonio: Stefano Coniglio.Google Scholar
  18. Parreno, F., D. Pacino, and R. Alvarez-Valdes. 2016. A GRASP algorithm for the container stowage slot planning problem. Transportation Research Part E: Logistics and Transportation Review 94: 141–157.CrossRefGoogle Scholar
  19. Rashidi, H., and E. Tsang. 2013. Novel constraints satisfaction models for optimization problems in container terminals. Applied Mathematical Model 37: 3601–3634.CrossRefGoogle Scholar
  20. Ryan, D’Arcy J. 2001. Strategic Alliances and Their Impacts On the Container Shipping Industry. Doctoral dissertation, Concordia UniversityGoogle Scholar
  21. Shi X., Meersman H., & Voss S. (2008). The win-win game in slot-chartering agreement among the liner competitors and collaborators, International Association of Maritime Economists.Google Scholar
  22. Stahlbock, R., and S. Voss. 2008. Operations research at container terminal: a literature update. OR Spectrum 30: 1–52.CrossRefGoogle Scholar
  23. Steenken, D., S. Voß, and R. Stahlbock. 2004. Container terminal operation and operations research—A classification and literature review. OR Spectrum 26 (1): 3–49.CrossRefGoogle Scholar
  24. Tierney, K., D. Pacino, and J.R. Møller. 2014. On the complexity of container stowage planning problems. Discrete applied mathematics 169: 225–230.CrossRefGoogle Scholar
  25. Wilson, D., and P.A. Roach. 2000. Container stowage planning: a methodology for generating computerised solutions. J Oper Res Soc 51 (11): 1248–1255.CrossRefGoogle Scholar
  26. Wilson, D., P.A. Roach, and J.A. Ware. 2001. Container stowage pre-planning: using search to generate solutions, a case study. Knowledge-Based Systems 14 (3–4): 137–145.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Economics and Business Studies, School of Social SciencesUniversity of GenoaGenoaItaly

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