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A Decision Model for Berth Allocation Under Uncertainty Considering Service Level Using an Adaptive Differential Evolution Algorithm

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  • Changchun Liu

    (Department of Industrial Engineering, Tsinghua University, Beijing 100084, P. R. China2Logistics Engineering and Simulation Laboratory, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, P. R. China)

  • Xi Xiang

    (Department of Industrial Engineering, Tsinghua University, Beijing 100084, P. R. China2Logistics Engineering and Simulation Laboratory, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, P. R. China)

  • Canrong Zhang

    (Logistics Engineering and Simulation Laboratory, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, P. R. China)

  • Li Zheng

    (Department of Industrial Engineering, Tsinghua University, Beijing 100084, P. R. China)

Abstract

This paper focuses on the berth allocation problem, which is to determine where and when the vessels to be loaded and unloaded at a terminal within a given planning horizon, with consideration of uncertain factors, mainly including the arrival and operation time of the calling vessels. Based on the concept of service level which is commonly used in the inventory system, a decision model is constructed to minimize the cost of baseline schedule, which includes delay cost and nonoptimal berthing location cost. According to the specific characteristics of the model, the upper and lower bounds are found. And due to the NP-hardness of the constructed model, an adaptive differential evolution is employed to solve the problem. Finally, extensive numerical experiments are conducted to test the performance of the proposed models and solution approaches.

Suggested Citation

  • Changchun Liu & Xi Xiang & Canrong Zhang & Li Zheng, 2016. "A Decision Model for Berth Allocation Under Uncertainty Considering Service Level Using an Adaptive Differential Evolution Algorithm," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(06), pages 1-28, December.
    • Handle: RePEc:wsi:apjorx:v:33:y:2016:i:06:n:s0217595916500494
    DOI: 10.1142/S0217595916500494
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    References listed on IDEAS

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    Cited by:

    1. 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.
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    3. Rodrigues, Filipe & Agra, Agostinho, 2021. "An exact robust approach for the integrated berth allocation and quay crane scheduling problem under uncertain arrival times," European Journal of Operational Research, Elsevier, vol. 295(2), pages 499-516.
    4. Agra, Agostinho & Rodrigues, Filipe, 2022. "Distributionally robust optimization for the berth allocation problem under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 1-24.
    5. Rodrigues, Filipe & Agra, Agostinho, 2022. "Berth allocation and quay crane assignment/scheduling problem under uncertainty: A survey," European Journal of Operational Research, Elsevier, vol. 303(2), pages 501-524.
    6. Xiang, Xi & Liu, Changchun, 2021. "An almost robust optimization model for integrated berth allocation and quay crane assignment problem," Omega, Elsevier, vol. 104(C).
    7. Xi, Xiang & Changchun, Liu & Yuan, Wang & Loo Hay, Lee, 2020. "Two-stage conflict robust optimization models for cross-dock truck scheduling problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    8. Xiang, Xi & Liu, Changchun & Miao, Lixin, 2017. "A bi-objective robust model for berth allocation scheduling under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 294-319.
    9. Canrong Zhang & Tao Wu & Mingyao Qi & Lixin Miao, 2018. "Simultaneous Allocation of Berths and Quay Cranes under Discrete Berth Situation," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(03), pages 1-28, June.

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