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Information-Based Allocation Strategy for GRID-Based Transshipment Automated Container Terminal

Author

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  • Chenhao Zhou

    (Department of Industrial and Systems Engineering, National University of Singapore, Singapore 117576)

  • Ek Peng Chew

    (Department of Industrial and Systems Engineering, National University of Singapore, Singapore 117576)

  • Loo Hay Lee

    (Department of Industrial and Systems Engineering, National University of Singapore, Singapore 117576)

Abstract

In this paper, we introduce a storage allocation strategy for a transshipment container hub using a new automated container terminal called the hybrid GRID system. This study provides a novel approach of developing an efficient storage allocation strategy for new terminal concepts so that the complex formulations can be approximated by simple functions that can be quickly computed. Specifically, the storage allocation strategy is derived from an optimal allocation decision learned from a Mixed Integer Programming (MIP) model. Some input parameters of the MIP model are collected from a simulation model. An index measuring the storage location convenience is proposed and we regress this index with important variables to build an empirical model that provides recommendations on where to allocate containers to storage locations. The advantage of using the empirical approach is that it allows for fast computation which is expected in the dynamic and uncertain port environment. Numerical results show that four variables can significantly affect the performance of the allocation decision. This strategy is shown to be robust for different scenarios in terms of terminal configuration and container workload. In addition, the new strategy performs better than the commonly used strategies found in warehousing literatures, and the solution is close to the optimal allocation decision.

Suggested Citation

  • Chenhao Zhou & Ek Peng Chew & Loo Hay Lee, 2018. "Information-Based Allocation Strategy for GRID-Based Transshipment Automated Container Terminal," Transportation Science, INFORMS, vol. 52(3), pages 707-721, June.
  • Handle: RePEc:inm:ortrsc:v:52:y:2018:i:3:p:707-721
    DOI: 10.1287/trsc.2017.0736
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    References listed on IDEAS

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    Cited by:

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    3. Wang, Zehao & Zeng, Qingcheng & Li, Xingchun & Qu, Chenrui, 2024. "A branch-and-price heuristic algorithm for the ART and external truck scheduling problem in an automated container terminal with a parallel layout," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    4. Feng, Yuanjun & Song, Dong-Ping & Li, Dong, 2022. "Smart stacking for import containers using customer information at automated container terminals," European Journal of Operational Research, Elsevier, vol. 301(2), pages 502-522.
    5. 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.
    6. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.

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