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A generic model for the local container drayage problem using the emerging truck platooning operation mode

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  • You, Jintao
  • Miao, Lixin
  • Zhang, Canrong
  • Xue, Zhaojie

Abstract

The local container drayage problem (LCDP) plays an important role in the waterborne transportation for global trade. Truck platooning is an emerging and promising container transportation mode in which a platoon is formed by a leading truck followed by a set of trucks using semi-automated technologies. This paper focuses on the truck platooning operation mode (TPOM) in which only the leading truck is human-driven. We also focus on attempts to exploit the advantages of the new operation mode, such as the ability to use multiple trucks simultaneously with only one driver. Moreover, one customer can be serviced by two different drivers through coordination, and one container can be shared among customers without returning to the depot. All of these advantages improve the LCDP operational performance. A mathematical model is proposed to capture these advantages and related features pertaining to this new operation mode. The model is compared, theoretically and numerically, with three well-studied LCDP models reported in the literature, and we prove that our model is a generalization of the previous three models. For even small-scale problems, CPLEX has difficulty achieving satisfactory solutions due to the NP-hardness and complicated structure of the problem. Therefore, a heuristic method that incorporates the learning mechanism based on the ant colony algorithm is proposed to overcome the problem. Extensive numerical experiments were conducted to validate the model and proposed methods, and the experimental results show that the benefits of the new operation mode can be achieved by increasing the platoon length and/or well coordinating the sharing of containers among customers.

Suggested Citation

  • You, Jintao & Miao, Lixin & Zhang, Canrong & Xue, Zhaojie, 2020. "A generic model for the local container drayage problem using the emerging truck platooning operation mode," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 181-209.
  • Handle: RePEc:eee:transb:v:133:y:2020:i:c:p:181-209
    DOI: 10.1016/j.trb.2019.12.009
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    References listed on IDEAS

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    1. Bustos-Coral, Daniel & Costa, Alysson M., 2022. "Drayage routing with heterogeneous fleet, compatibility constraints, and truck load configurations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    2. Yan, Xiaoyuan & Xu, Min & Xie, Chi, 2023. "Local container drayage problem with improved truck platooning operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    3. Bouchery, Yann & Hezarkhani, Behzad & Stauffer, Gautier, 2022. "Coalition formation and cost sharing for truck platooning," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 15-34.
    4. Scholl, Joachim & Boysen, Nils & Scholl, Armin, 2023. "E-platooning: Optimizing platoon formation for long-haul transportation with electric commercial vehicles," European Journal of Operational Research, Elsevier, vol. 304(2), pages 525-542.
    5. Guan, Hao & Wang, Hua & Meng, Qiang & Mak, Chin Long, 2023. "Markov chain-based traffic analysis on platooning effect among mixed semi- and fully-autonomous vehicles in a freeway lane," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 176-202.
    6. Chen, Shukai & Wang, Hua & Meng, Qiang, 2023. "Cost allocation of cooperative autonomous truck platooning: Efficiency and stability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 119-141.
    7. Chen, Rui & Meng, Qiang & Jia, Peng, 2022. "Container port drayage operations and management: Past and future," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    8. Roy, Debjit & van Ommeren, Jan-Kees & de Koster, René & Gharehgozli, Amir, 2022. "Modeling landside container terminal queues: Exact analysis and approximations," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 73-102.
    9. You, Jintao & Wang, Yuan & Xue, Zhaojie, 2023. "An exact algorithm for the multi-trip container drayage problem with truck platooning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    10. Chen, Rui & Jia, Shuai & Meng, Qiang, 2023. "Dynamic container drayage booking and routing decision support approach for E-commerce platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    11. Chen, Shukai & Wang, Hua & Meng, Qiang, 2021. "Autonomous truck scheduling for container transshipment between two seaport terminals considering platooning and speed optimization," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 289-315.
    12. Jia, Shuai & Cui, Haipeng & Chen, Rui & Meng, Qiang, 2022. "Dynamic container drayage with uncertain request arrival times and service time windows," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 237-258.
    13. Boshuai Zhao & Roel Leus, 2022. "An improved decomposition-based heuristic for truck platooning," Papers 2210.05562, arXiv.org, revised Feb 2023.
    14. Xue, Zhaojie & Lin, Hui & You, Jintao, 2021. "Local container drayage problem with truck platooning mode," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).

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