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Container port drayage operations and management: Past and future

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  • Chen, Rui
  • Meng, Qiang
  • Jia, Peng

Abstract

Although long-haul intermodal container transportation combines multiple transportation modes including shipping, the most critical aspect of enabling door-to-door service is container port drayage operation. Container port drayage connects customers and container ports and provides an effective solution to the first/last-mile problem faced by the container logistics industry. Therefore, container port drayage has a significant impact not only on the economic advantages of the long-haul intermodal container transportation chain, but also the management, environment and social benefits of the stakeholders involved. In this paper, we comprehensively review the existing literature on container port drayage studies from different methodological angles (e.g., model development and algorithm design) and technological angles (e.g., autonomous truck and blockchain). Moreover, with the shifting of industrial focus from traditional operations and management of container port drayage towards e-commerce platforms, we identify potential issues for future research directions.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:transe:v:159:y:2022:i:c:s136655452200031x
    DOI: 10.1016/j.tre.2022.102633
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    5. 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).
    6. 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).
    7. Wang, Haibo & Alidaee, Bahram, 2023. "White-glove service delivery: A quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    8. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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