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A scenario-based dynamic programming model for multi-period liner ship fleet planning

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  • Meng, Qiang
  • Wang, Tingsong
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    Abstract

    This paper proposes a more realistic multi-period liner ship fleet planning problem for a liner container shipping company than has been studied in previous literature. The proposed problem is formulated as a scenario-based dynamic programming model consisting of a number of integer linear programming formulations for each single planning period, and the model can be solved efficiently by a shortest path algorithm on an acyclic network. A numerical example is carried out to illustrate the applicability of the proposed model and solution method. The numerical results show that chartering in ships may not always be a better policy for a long-term planning horizon though it is much cheaper than buying ships in the short-term. Purchasing ships seems to be a more profitable investment in the long run.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part E: Logistics and Transportation Review.

    Volume (Year): 47 (2011)
    Issue (Month): 4 (July)
    Pages: 401-413

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    Handle: RePEc:eee:transe:v:47:y:2011:i:4:p:401-413

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    Related research

    Keywords: Multi-period liner ship fleet planning Scenario decision tree Dynamic programming model Integer linear programming model;

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    Cited by:
    1. Song, Dong-Ping & Dong, Jing-Xin, 2012. "Cargo routing and empty container repositioning in multiple shipping service routes," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1556-1575.
    2. Meng, Qiang & Wang, Shuaian, 2012. "Liner ship fleet deployment with week-dependent container shipment demand," European Journal of Operational Research, Elsevier, vol. 222(2), pages 241-252.
    3. Meng, Qiang & Wang, Tingsong & Wang, Shuaian, 2012. "Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand," European Journal of Operational Research, Elsevier, vol. 223(1), pages 96-105.
    4. Pantuso, Giovanni & Fagerholt, Kjetil & Hvattum, Lars Magnus, 2014. "A survey on maritime fleet size and mix problems," European Journal of Operational Research, Elsevier, vol. 235(2), pages 341-349.
    5. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship fleet deployment with container transshipment operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 470-484.
    6. Wang, Shuaian & Meng, Qiang, 2012. "Sailing speed optimization for container ships in a liner shipping network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 701-714.
    7. Christiansen, Marielle & Fagerholt, Kjetil & Nygreen, Bjørn & Ronen, David, 2013. "Ship routing and scheduling in the new millennium," European Journal of Operational Research, Elsevier, vol. 228(3), pages 467-483.
    8. Wang, Shuaian & Meng, Qiang, 2012. "Robust schedule design for liner shipping services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1093-1106.
    9. Wang, Tingsong & Meng, Qiang & Wang, Shuaian & Tan, Zhijia, 2013. "Risk management in liner ship fleet deployment: A joint chance constrained programming model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 60(C), pages 1-12.
    10. Wang, Shuaian, 2014. "A novel hybrid-link-based container routing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 165-175.
    11. Wang, Shuaian, 2013. "Essential elements in tactical planning models for container liner shipping," Transportation Research Part B: Methodological, Elsevier, vol. 54(C), pages 84-99.
    12. Wang, Shuaian & Meng, Qiang & Sun, Zhuo, 2013. "Container routing in liner shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 1-7.
    13. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship route schedule design with sea contingency time and port time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 615-633.

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