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(s,S) policy model for liner shipping refueling and sailing speed optimization problem

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  • Sheng, Xiaoming
  • Chew, Ek Peng
  • Lee, Loo Hay

Abstract

This work expounds on implementing an effective dynamic (s,S) policy to solve a liner shipping refueling and speed determination problem under both bunker prices and consumption uncertainties. While solving an optimization model which incorporates a continuous distribution is extremely challenging, we use sample average approximation method to solve it. However, the resulting problem is still a very large-scaled problem. Therefore, we propose two variations of the progressive hedging algorithm to tackle it. Numerical results show that our solution method is efficient and, in addition, our dynamic (s,S) policy model has significant cost reduction potential compared to stationary models.

Suggested Citation

  • Sheng, Xiaoming & Chew, Ek Peng & Lee, Loo Hay, 2015. "(s,S) policy model for liner shipping refueling and sailing speed optimization problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 76(C), pages 76-92.
    • Handle: RePEc:eee:transe:v:76:y:2015:i:c:p:76-92
    DOI: 10.1016/j.tre.2014.12.001
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    Cited by:

    1. Ali Cheaitou & Pierre Cariou, 2019. "Greening of maritime transportation: a multi-objective optimization approach," Annals of Operations Research, Springer, vol. 273(1), pages 501-525, February.
    2. Tan, Roy & Duru, Okan & Thepsithar, Prapisala, 2020. "Assessment of relative fuel cost for dual fuel marine engines along major Asian container shipping routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    3. Li, Chen & Qi, Xiangtong & Song, Dongping, 2016. "Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 762-788.
    4. Kazemi, Ahmad & Ernst, Andreas T. & Krishnamoorthy, Mohan & Le Bodic, Pierre, 2021. "Locomotive fuel management with inline refueling," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1077-1096.
    5. Junayed Pasha & Maxim A. Dulebenets & Masoud Kavoosi & Olumide F. Abioye & Oluwatosin Theophilus & Hui Wang & Raphael Kampmann & Weihong Guo, 2020. "Holistic tactical-level planning in liner shipping: an exact optimization approach," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-35, December.
    6. Mulder, J. & Dekker, R., 2016. "Optimization in container liner shipping," Econometric Institute Research Papers EI2016-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Fuentes, Gabriel, 2021. "Generating bunkering statistics from AIS data: A machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    8. Zhen, Lu & Wang, Shuaian & Zhuge, Dan, 2017. "Dynamic programming for optimal ship refueling decision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 63-74.

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