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Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning

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  • Hongtao Hu
  • Xurui Yang
  • Shichang Xiao
  • Feiyang Wang

Abstract

AGV conflict prevention path planning is a key factor to improve transportation cost and operation efficiency of the container terminal. This paper studies the anti-conflict path planning problem of Automated Guided Vehicle (AGV) in the horizontal transportation area of the Automated Container Terminals (ACTs). According to the characteristics of magnetic nail guided AGVs, a node network is constructed. Through the analysis of two conflict situations, namely the opposite conflict situation and same point occupation conflict situation, an integer programming model is established to obtain the shortest path. The Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is proposed to solve the problem, and the Gumbel-Softmax strategy is applied to discretize the scenario created by the node network. A series of numerical experiments are conducted to verify the effectiveness and the efficiency of the model and the algorithm.

Suggested Citation

  • Hongtao Hu & Xurui Yang & Shichang Xiao & Feiyang Wang, 2023. "Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning," International Journal of Production Research, Taylor & Francis Journals, vol. 61(1), pages 65-80, January.
    • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:1:p:65-80
    DOI: 10.1080/00207543.2021.1998695
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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.
    2. Li, Kunpeng & Liu, Tengbo & Ram Kumar, P.N. & Han, Xuefang, 2024. "A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    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).

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