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Bi-objective optimization for the container terminal integrated planning

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  • Liu, Ming
  • Lee, Chung-Yee
  • Zhang, Zizhen
  • Chu, Chengbin

Abstract

In this paper, we study the joint optimization of the tactical berth allocation and the tactical yard allocation in container terminals, which typically consist of berth side and yard side operations. The studied two objectives are: (i) the minimization of the violation of the vessels’ expected turnaround time windows with the purpose of meeting the timetables published by shipping liners, and (ii) the minimization of the total yard transportation distance with the aim to lower terminal operational cost. We propose a bi-objective integer program which can comprehensively address the import, export and transshipment tasks in port daily practice. Traditionally, a container transshipment task is performed as a couple of import and export tasks, called indirect-transshipment mode, in which the transit container are needed to be temporally stored in the yard. As the way of transferring containers directly from the incoming vessel to the outgoing vessel, called direct-transshipment mode, has potential to save yard storage resources, the proposed model also incorporates both indirect- and direct-transshipment modes. To produce Pareto solutions efficiently, we devise heuristic approaches. Numerical experiments have been conducted to demonstrate the efficiency of the approaches.

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  • Liu, Ming & Lee, Chung-Yee & Zhang, Zizhen & Chu, Chengbin, 2016. "Bi-objective optimization for the container terminal integrated planning," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 720-749.
  • Handle: RePEc:eee:transb:v:93:y:2016:i:pb:p:720-749
    DOI: 10.1016/j.trb.2016.05.012
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    References listed on IDEAS

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    14. Shi, Ning & Zhou, Shaorui & Wang, Fan & Tao, Yi & Liu, Liming, 2017. "The multi-criteria constrained shortest path problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 13-29.
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    16. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    17. Zhen, Lu & Hu, Yi & Wang, Shuaian & Laporte, Gilbert & Wu, Yiwei, 2019. "Fleet deployment and demand fulfillment for container shipping liners," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 15-32.
    18. Silva, Marcos de Melo da & Erdoğan, Güneş & Battarra, Maria & Strusevich, Vitaly, 2018. "The Block Retrieval Problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 931-950.
    19. Zhang, Canrong & Guan, Hao & Yuan, Yifei & Chen, Weiwei & Wu, Tao, 2020. "Machine learning-driven algorithms for the container relocation problem," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 102-131.
    20. Zhen, Lu & Liang, Zhe & Zhuge, Dan & Lee, Loo Hay & Chew, Ek Peng, 2017. "Daily berth planning in a tidal port with channel flow control," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 193-217.
    21. Yang, Songpo & Liao, Feixiong & Wu, Jianjun & Timmermans, Harry J.P. & Sun, Huijun & Gao, Ziyou, 2020. "A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 85-113.

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