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Data-driven dynamic stacking strategy for export containers in container terminals

Author

Listed:
  • Hyun Ji Park

    (Korea Research Institute of Ships and Ocean Engineering)

  • Sung Won Cho

    (Korea Research Institute of Ships and Ocean Engineering)

  • Abhilasha Nanda

    (Korea Research Institute of Ships and Ocean Engineering)

  • Jin Hyoung Park

    (Korea Research Institute of Ships and Ocean Engineering)

Abstract

This study investigates a method for improving real-time decisions regarding the storage location of export containers while the containers are arriving. To manage the decision-making process, we propose a two module-based data-driven dynamic stacking strategy that facilitates stowage planning. Module 1 generates the Gaussian mixture model (GMM) specific to each container group for container weight classification. Module 2 implements the data-driven dynamic stacking strategy as an online algorithm to determine the storage location of an arriving container in real time. Numerical experiments were conducted using real-life data to validate the effectiveness of the proposed method compared to other alternative stacking strategies. These experiments revealed that the performance of the proposed method is robust, and therefore it can improve yard operations and container terminal competitiveness.

Suggested Citation

  • Hyun Ji Park & Sung Won Cho & Abhilasha Nanda & Jin Hyoung Park, 2023. "Data-driven dynamic stacking strategy for export containers in container terminals," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 170-195, March.
    • Handle: RePEc:spr:flsman:v:35:y:2023:i:1:d:10.1007_s10696-022-09457-8
    DOI: 10.1007/s10696-022-09457-8
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    References listed on IDEAS

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