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Optimization for a Multi-Constraint Truck Appointment System Considering Morning and Evening Peak Congestion

Author

Listed:
  • Bowei Xu

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Xiaoyan Liu

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Yongsheng Yang

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Junjun Li

    (College of Merchant Marine, Shanghai Maritime University, Shanghai 201306, China)

  • Octavian Postolache

    (ISCTE, Lisbon University Institute, 1649-026 Lisboa, Portugal)

Abstract

Gate and yard congestion is a typical type of container port congestion, which prevents trucks from traveling freely and has become the bottleneck that constrains the port productivity. In addition, urban traffic increases the uncertainty of the truck arrival time and additional congestion costs. More and more container terminals are adopting a truck appointment system (TAS), which tries to manage the truck arrivals evenly all day long. Extending the existing research, this work considers morning and evening peak congestion and proposes a novel approach for multi-constraint TAS intended to serve both truck companies and container terminals. A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. The aim of the proposed multi-constraint TAS model is to minimize the overall operation cost. The Lingo commercial software is used to solve the exact solutions for small and medium scale problems, and a hybrid genetic algorithm and simulated annealing (HGA-SA) is proposed to obtain the solutions for large-scale problems. Experimental results indicate that the proposed TAS can not only better serve truck companies and container terminals but also more effectively reduce their overall operation cost compared with the traditional TASs.

Suggested Citation

  • Bowei Xu & Xiaoyan Liu & Yongsheng Yang & Junjun Li & Octavian Postolache, 2021. "Optimization for a Multi-Constraint Truck Appointment System Considering Morning and Evening Peak Congestion," Sustainability, MDPI, vol. 13(3), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1181-:d:485678
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    References listed on IDEAS

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    Cited by:

    1. Lange, Ann-Kathrin & Nellen, Nicole & Jahn, Carlos, 2022. "Truck appointment systems: How can they be improved and what are their limits?," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 615-655, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Feng, Xuehao & He, Yucheng & Kim, Kap-Hwan, 2022. "Space planning considering congestion in container terminal yards," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 52-77.
    3. Weiguang Mu & Chengzhu Gong, 2023. "A Data-Driven Approach to W-Beam Barrier Monitoring Data Processing: A Case Study of Highway Congestion Mitigation Strategy," Sustainability, MDPI, vol. 15(5), pages 1-14, February.
    4. Karol Moszyk & Mariusz Deja & Michal Dobrzynski, 2021. "Automation of the Road Gate Operations Process at the Container Terminal—A Case Study of DCT Gdańsk SA," Sustainability, MDPI, vol. 13(11), pages 1-19, June.

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