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Bunker purchasing with contracts

Author

Listed:
  • Christian Edinger Munk Plum

    (1] Maersk Line, Esplanaden 50, 1098 Copenhagen K, Denmark.[2] Management Engineering, Technical University of Denmark, Produktionstorvet 426, 2800 Kgs. Lyngby, Denmark)

  • Peter Neergaard Jensen

    (Maersk Oil Trading, Esplanaden 50, 1098 Copenhagen K, Denmark)

  • David Pisinger

    (Management Engineering, Technical University of Denmark, Produktionstorvet 426, 2800 Kgs. Lyngby, Denmark)

Abstract

The cost for bunker fuel represents a major part of the daily running costs of liner shipping vessels. The vessels, sailing on a fixed roundtrip of ports, can lift bunker at these ports, having differing and fluctuating prices. The stock of bunker on a vessel is subject to a number of operational constraints such as capacity limits, reserve requirements and sulphur content. Contracts are often used for bunker purchasing, ensuring supply and often giving a discounted price. A contract can supply any vessel in a period and port, and is thus a shared resource between vessels, which must be distributed optimally to reduce overall costs. The Bunker Purchasing with Contracts Problem has been formulated as a mixed integer programme, which has been Dantzig-Wolfe decomposed. To solve it, a novel column generation algorithm has been developed. The algorithm has been run on a series of real-world instances with up to 500+ vessels and 500+ contracts, and provide near optimal solutions. This makes it possible for a major liner shipping company to plan bunker purchasing on a global level, and provides an efficient tool for assessing new contracts.

Suggested Citation

  • Christian Edinger Munk Plum & Peter Neergaard Jensen & David Pisinger, 2014. "Bunker purchasing with contracts," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 16(4), pages 418-435, December.
    • Handle: RePEc:pal:marecl:v:16:y:2014:i:4:p:418-435
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    Citations

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    Cited by:

    1. Gu, Yewen & Wallace, Stein W. & Wang, Xin, 2016. "Integrated maritime bunker management with stochastic fuel prices and new emission regulations," Discussion Papers 2016/13, Norwegian School of Economics, Department of Business and Management Science.
    2. Wang, Yadong & Meng, Qiang, 2021. "Optimizing freight rate of spot market containers with uncertainties in shipping demand and available ship capacity," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 314-332.
    3. Wang, Shuaian & Meng, Qiang, 2015. "Robust bunker management for liner shipping networks," European Journal of Operational Research, Elsevier, vol. 243(3), pages 789-797.
    4. 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).
    5. Pedrielli, Giulia & Lee, Loo Hay & Ng, Szu Hui, 2015. "Optimal bunkering contract in a buyer–seller supply chain under price and consumption uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 77-94.
    6. 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.
    7. Ghosh, Sugoutam & Lee, Loo Hay & Ng, Szu Hui, 2015. "Bunkering decisions for a shipping liner in an uncertain environment with service contract," European Journal of Operational Research, Elsevier, vol. 244(3), pages 792-802.
    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.
    9. 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.

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