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Managing customer arrivals with time windows: a case of truck arrivals at a congested container terminal

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  • Gang Chen

    (Aalborg University)

  • Liping Jiang

    (Copenhagen Business School)

Abstract

Due to increasing container traffic and mega-ships, many seaports face challenges of huge amounts of truck arrivals and congestion problem at terminal gates, which affect port efficiency and generate serious air pollution. To solve this congestion problem, we propose a solution of managing truck arrivals with time windows based on the truck-vessel service relationship, specifically trucks delivering containers for the same vessel share one common time window. Time windows can be optimized with different strategies. In this paper, we first propose a framework for installing this solution in a terminal system, and second develop an optimization model for scaling time windows with three alternative strategies: namely fixed ending-point strategy (FEP), variable end-point strategy and greedy algorithm strategy. Third, to compare the strategies in terms of effectiveness, numerical experiments are conducted based on real data. The result shows that (1) good planning coordination is essential for the proposed method; and (2) FEP is found to be a better strategy than the other two.

Suggested Citation

  • Gang Chen & Liping Jiang, 2016. "Managing customer arrivals with time windows: a case of truck arrivals at a congested container terminal," Annals of Operations Research, Springer, vol. 244(2), pages 349-365, September.
    • Handle: RePEc:spr:annopr:v:244:y:2016:i:2:d:10.1007_s10479-016-2150-3
    DOI: 10.1007/s10479-016-2150-3
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    References listed on IDEAS

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    Cited by:

    1. Mengzhi Ma & Houming Fan & Xiaodan Jiang & Zhenfeng Guo, 2019. "Truck Arrivals Scheduling with Vessel Dependent Time Windows to Reduce Carbon Emissions," Sustainability, MDPI, vol. 11(22), pages 1-26, November.
    2. Mohammad Torkjazi & Nathan Huynh & Ali Asadabadi, 2022. "Modeling the Truck Appointment System as a Multi-Player Game," Logistics, MDPI, vol. 6(3), pages 1-25, July.
    3. Shan, Wenxuan & Peng, Zixuan & Liu, Jiaming & Yao, Baozhen & Yu, Bin, 2020. "An exact algorithm for inland container transportation network design," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 41-82.
    4. Lange, Ann-Kathrin & Kreuz, Felix & Langkau, Sven & Jahn, Carlos & Clausen, Uwe, 2020. "Defining the quota of truck appointment systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 211-246, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    5. 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.
    6. Budhi S. Wibowo & Jan C. Fransoo, 2023. "Performance analysis of a drop-swap terminal to mitigate truck congestion at chemical sites," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 416-454, June.

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