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Heuristic-assisted deep learning for integrated scheduling of multiple equipment in automated container terminals

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  • Ho Van Roi

    (Korea Maritime and Ocean University, Department of Convergence Interdisciplinary Education of Maritime & Ocean Contents (Logistics System)
    Korea Maritime and Ocean University, Department of Logistics)

  • Sam-Sang You

    (Vietnam National University (VNU)-Ho Chi Minh City (HCM)
    Ho Chi Minh City University of Technology (HCMUT), Department of Mechatronics, Faculty of Mechanical Engineering
    Korea Maritime and Ocean University, Division of Mechanical Engineering
    Korea Maritime and Ocean University, Northeast-Asia Shipping and Port Logistics Research Center)

  • Hwan-Seong Kim

    (Korea Maritime and Ocean University, Department of Logistics)

  • Le Ngoc Bao Long

    (Korea Maritime and Ocean University, Department of Convergence Interdisciplinary Education of Maritime & Ocean Contents (Logistics System)
    Korea Maritime and Ocean University, Human Resource Training Research Group for Convergence in Global Ocean Logistics
    Korea Maritime and Ocean University, Northeast-Asia Shipping and Port Logistics Research Center)

  • Truong Ngoc Cuong

    (Vietnam National University (VNU)-Ho Chi Minh City (HCM)
    Ho Chi Minh City University of Technology (HCMUT), Department of Mechatronics, Faculty of Mechanical Engineering)

  • Duy Anh Nguyen

    (Vietnam National University (VNU)-Ho Chi Minh City (HCM)
    Ho Chi Minh City University of Technology (HCMUT), Department of Mechatronics, Faculty of Mechanical Engineering)

Abstract

Incorporating AI-powered algorithms into business operations is becoming inevitable to gain a comparative advantage in the global business landscape. In this paper, we minimize the total travel distance and time of automated guided vehicles (AGVs) by coordinating routes and waiting time, using a hybrid method that integrates deep reinforcement learning (DRL) and heuristic search in an automated container terminal (ACT). Modern technology and data analytics provide intelligent, automated solutions for managing port resources such as berths, AGVs, quay cranes, and yard cranes. The deep deterministic policy gradient (DDPG) and advanced A-star search algorithms are employed, enhancing stochastic sequential decision-making for integrated scheduling and routing of port equipment. Our test results confirm the effectiveness of the hybrid approach for optimizing port resources and services. Decision-making strategies driven by a hybrid algorithm can optimize container handling and improve operational efficiency for competent maritime logistics. This paradigm shift in business operations contributes to the growing field of intelligent port management by paving the way for adaptive and innovative solutions in the maritime logistics industry, enhancing performance and productivity.

Suggested Citation

  • Ho Van Roi & Sam-Sang You & Hwan-Seong Kim & Le Ngoc Bao Long & Truong Ngoc Cuong & Duy Anh Nguyen, 2025. "Heuristic-assisted deep learning for integrated scheduling of multiple equipment in automated container terminals," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 27(4), pages 786-818, December.
    • Handle: RePEc:pal:marecl:v:27:y:2025:i:4:d:10.1057_s41278-025-00327-2
    DOI: 10.1057/s41278-025-00327-2
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