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Terminal allocation problem in a transshipment hub considering bunker consumption

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  • Lu Zhen
  • Shuaian Wang
  • Kai Wang

Abstract

This article investigates the method of allocating arriving vessels to the terminals in transshipment hubs. The terminal allocation decision faced by a shipping alliance has the influence on the scheduled arrival time of vessels and further affects the bunker consumption cost for the vessels. A model is formulated to minimize the bunker consumption cost as well as the transportation cost of inter‐terminal transshipment flows/movements. The capacity limitation of the port resources such as quay cranes (QCs) and berths is taken into account. Besides the terminal allocation, the QC assignment decision is also incorporated in the proposed model. A local branching based method and a particle swarm optimization based method are developed to solve the model in large‐scale problem instances. Numerical experiments are also conducted to validate the effectiveness of the proposed model, which can save around 14% of the cost when compared with the “First Come First Served” decision rule. Moreover, the proposed solution methods not only solve the proposed model within a reasonable computation time, but also obtain near‐optimal results with about 0.1∼0.7% relative gap. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 529–548, 2016

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  • Lu Zhen & Shuaian Wang & Kai Wang, 2016. "Terminal allocation problem in a transshipment hub considering bunker consumption," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(7), pages 529-548, October.
    • Handle: RePEc:wly:navres:v:63:y:2016:i:7:p:529-548
    DOI: 10.1002/nav.21717
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    Cited by:

    1. H. L. Ma & S. H. Chung & H. K. Chan & Li Cui, 2019. "An integrated model for berth and yard planning in container terminals with multi-continuous berth layout," Annals of Operations Research, Springer, vol. 273(1), pages 409-431, February.
    2. Xu Zhao & Qianjun Lin & Hao Yu, 2019. "An Improved Mathematical Model for Green Lock Scheduling Problem of the Three Gorges Dam," Sustainability, MDPI, vol. 11(9), pages 1-23, May.
    3. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    4. Changchun Liu & Xi Xiang & Li Zheng, 2017. "Two decision models for berth allocation problem under uncertainty considering service level," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 312-344, December.
    5. Zhen, Lu & Wang, Wencheng & Lin, Shumin, 2022. "Analytical comparison on two incentive policies for shore power equipped ships in berthing activities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    6. Wang, Tingsong & Wang, Xinchang & Meng, Qiang, 2018. "Joint berth allocation and quay crane assignment under different carbon taxation policies," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 18-36.
    7. Guo, Liming & Zheng, Jianfeng & Liang, Jinpeng & Wang, Shuaian, 2023. "Column generation for the multi-port berth allocation problem with port cooperation stability," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 3-28.
    8. Guo, Liming & Zheng, Jianfeng & Du, Jian & Gao, Ziyou & Fagerholt, Kjetil, 2024. "Integrated planning of berth allocation, quay crane assignment and yard assignment in multiple cooperative terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).

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