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Berth and quay crane allocation and scheduling with worker performance variability and yard truck deployment in container terminals

Author

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  • Chargui, Kaoutar
  • Zouadi, Tarik
  • El Fallahi, Abdellah
  • Reghioui, Mohamed
  • Aouam, Tarik

Abstract

In a container terminal, the quay cranes (QCs) are the main equipment involved in the core activities. According to a bottleneck principle, the productivity rate of QCs is related to both worker productivity and deployed yard trucks. Yet, in the literature, the berth and quay cranes allocation and scheduling problems were addressed separately from these two parameters. In contrast, practitioners confirm their important impact on cranes productivity and efficiency. This paper proposes a new extension of the berth and quay cranes allocation and scheduling problems considering worker performance variability and yard truck deployment constraints. First, we formulate the problem as a mixed-integer linear program to minimize the vessels departure time under many practical regulations involving the work roster constraints specific to container terminals and the trucks utilization congestion targets. Additionally, the study provides a computationally efficient method to find a lower bound. We also show that integrative planning minimizes the vessels departure time compared to the separated decision-making process. Second, to build good solutions in a reasonable time, we propose a heuristic and a Variable Neighborhood Search (VNS) with a new architecture and settings designed to suit the novel issues of the tackled problem. The algorithm is tested on real time data sets and outperforms a commercial solver. This study originates from our experience with a multinational company managing a container terminal. Thus, we have embedded the proposed resolving methods in a decision-support system for our industrial partner.

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  • Chargui, Kaoutar & Zouadi, Tarik & El Fallahi, Abdellah & Reghioui, Mohamed & Aouam, Tarik, 2021. "Berth and quay crane allocation and scheduling with worker performance variability and yard truck deployment in container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
  • Handle: RePEc:eee:transe:v:154:y:2021:i:c:s1366554521002131
    DOI: 10.1016/j.tre.2021.102449
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    References listed on IDEAS

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    2. Cheng Hong & Yufang Guo & Yuhong Wang & Tingting Li, 2023. "The Integrated Scheduling Optimization for Container Handling by Using Driverless Electric Truck in Automated Container Terminal," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
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    4. Chargui, Kaoutar & Zouadi, Tarik & Sreedharan, V. Raja & El Fallahi, Abdellah & Reghioui, Mohamed, 2023. "A novel robust exact decomposition algorithm for berth and quay crane allocation and scheduling problem considering uncertainty and energy efficiency," Omega, Elsevier, vol. 118(C).
    5. Meixian Jiang & Jiajia Feng & Jian Zhou & Lin Zhou & Fangzheng Ma & Guanghua Wu & Yuqiu Zhang, 2023. "Multi-Terminal Berth and Quay Crane Joint Scheduling in Container Ports Considering Carbon Cost," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    6. 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.
    7. Abou Kasm, Omar & Diabat, Ali & Chow, Joseph Y.J., 2023. "Simultaneous operation of next-generation and traditional quay cranes at container terminals," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1110-1125.
    8. Xing, Zheng & Liu, Haitao & Wang, Tingsong & Chew, Ek Peng & Lee, Loo Hay & Tan, Kok Choon, 2023. "Integrated automated guided vehicle dispatching and equipment scheduling with speed optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    9. Hao, Luyao & Jin, Jian Gang & Zhao, Ke, 2023. "Joint scheduling of barges and tugboats for river–sea intermodal transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).

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