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Multi-AGV Dynamic Scheduling in an Automated Container Terminal: A Deep Reinforcement Learning Approach

Author

Listed:
  • Xiyan Zheng

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Chengji Liang

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Yu Wang

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Jian Shi

    (Department of Engineering Technology, University of Houston, Houston, TX 77004, USA)

  • Gino Lim

    (Department of Industrial Engineering, University of Houston, Houston, TX 77004, USA)

Abstract

With the rapid development of global trade, ports and terminals are playing an increasingly important role, and automatic guided vehicles (AGVs) have been used as the main carriers performing the loading/unloading operations in automated container terminals. In this paper, we investigate a multi-AGV dynamic scheduling problem to improve the terminal operational efficiency, considering the sophisticated complexity and uncertainty involved in the port terminal operation. We propose to model the dynamic scheduling of AGVs as a Markov decision process (MDP) with mixed decision rules. Then, we develop a novel adaptive learning algorithm based on a deep Q-network (DQN) to generate the optimal policy. The proposed algorithm is trained based on data obtained from interactions with a simulation environment that reflects the real-world operation of an automated in Shanghai, China. The simulation studies show that, compared with conventional scheduling methods using a heuristic algorithm, i.e., genetic algorithm (GA) and rule-based scheduling, terminal the proposed approach performs better in terms of effectiveness and efficiency.

Suggested Citation

  • Xiyan Zheng & Chengji Liang & Yu Wang & Jian Shi & Gino Lim, 2022. "Multi-AGV Dynamic Scheduling in an Automated Container Terminal: A Deep Reinforcement Learning Approach," Mathematics, MDPI, vol. 10(23), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4575-:d:992042
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    References listed on IDEAS

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    Cited by:

    1. Jin, Jiahuan & Cui, Tianxiang & Bai, Ruibin & Qu, Rong, 2024. "Container port truck dispatching optimization using Real2Sim based deep reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(1), pages 161-175.

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