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Integrated reinforcement learning of automated guided vehicles dynamic path planning for smart logistics and operations

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  • Ho, G.T.S.
  • Tang, Yuk Ming
  • Leung, Eric K.H.
  • Tong, P.H.

Abstract

Automated guided vehicles (AGV) play a critical role in fostering a smarter logistics and operations environment. Conventional path planning for AGVs enables the load-in-load-out of the items, but existing approaches rarely consider dynamic integrations with smart warehouses and factory systems. Therefore, this study presents a reinforcement learning (RL) approach for real-time path planning in automated guided vehicles within smart warehouses or smart factories. Unlike conventional path planning methods, which struggle to adapt to dynamic operational changes, the proposed algorithm integrates real-time information to enable responsive and flexible routing decisions. The novelty of this study lies in integrating AGV path planning and RL within a dynamic environment, such as a smart warehouse containing various workstations, charging stations, and storage locations. Through various scenarios in smart factory settings, this research demonstrates the algorithm’s effectiveness in handling complex logistics and operations environments. This research advances AGV technology by providing a scalable solution for dynamic path planning, enhancing efficiency in modern industrial systems.

Suggested Citation

  • Ho, G.T.S. & Tang, Yuk Ming & Leung, Eric K.H. & Tong, P.H., 2025. "Integrated reinforcement learning of automated guided vehicles dynamic path planning for smart logistics and operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:transe:v:196:y:2025:i:c:s1366554525000493
    DOI: 10.1016/j.tre.2025.104008
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    1. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    2. Zhen Shi & Keyin Wang & Jianhui Zhang, 2023. "Improved reinforcement learning path planning algorithm integrating prior knowledge," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-11, May.
    3. Hokey Min, 2023. "Smart Warehousing as a Wave of the Future," Logistics, MDPI, vol. 7(2), pages 1-12, May.
    4. Xie, Jiaohong & Yang, Zhenyu & Lai, Xiongfei & Liu, Yang & Yang, Xiao Bo & Teng, Teck-Hou & Tham, Chen-Khong, 2022. "Deep reinforcement learning for dynamic incident-responsive traffic information dissemination," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    5. Jianming Cai & Xiaokang Li & Yue Liang & Shan Ouyang, 2021. "Collaborative Optimization of Storage Location Assignment and Path Planning in Robotic Mobile Fulfillment Systems," Sustainability, MDPI, vol. 13(10), pages 1-26, May.
    6. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    7. Shen, Lixin & Wang, Yaodong & Liu, Kunpeng & Yang, Zaili & Shi, Xiaowen & Yang, Xu & Jing, Ke, 2020. "Synergistic path planning of multi-UAVs for air pollution detection of ships in ports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    8. 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).
    9. Chen, Xinwei & Ulmer, Marlin W. & Thomas, Barrett W., 2022. "Deep Q-learning for same-day delivery with vehicles and drones," European Journal of Operational Research, Elsevier, vol. 298(3), pages 939-952.
    10. Sven Winkelhaus & Eric H. Grosse, 2020. "Logistics 4.0: a systematic review towards a new logistics system," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 18-43, January.
    11. Wang, Dujuan & Wang, Qi & Yin, Yunqiang & Cheng, T.C.E., 2023. "Optimization of ride-sharing with passenger transfer via deep reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    12. Firdausiyah, N. & Taniguchi, E. & Qureshi, A.G., 2019. "Modeling city logistics using adaptive dynamic programming based multi-agent simulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 74-96.
    13. Yan, Yimo & Chow, Andy H.F. & Ho, Chin Pang & Kuo, Yong-Hong & Wu, Qihao & Ying, Chengshuo, 2022. "Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    14. Winkelhaus, S. & Grosse, E. H., 2020. "Logistics 4.0: a systematic review towards a new logistics system," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 118539, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Yan, Yimo & Deng, Yang & Cui, Songyi & Kuo, Yong-Hong & Chow, Andy H.F. & Ying, Chengshuo, 2023. "A policy gradient approach to solving dynamic assignment problem for on-site service delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    16. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    17. Zengliang Han & Dongqing Wang & Feng Liu & Zhiyong Zhao, 2017. "Multi-AGV path planning with double-path constraints by using an improved genetic algorithm," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    18. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    19. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    20. Antonio Falcó & Lucía Hilario & Nicolás Montés & Marta C. Mora & Enrique Nadal, 2020. "A Path Planning Algorithm for a Dynamic Environment Based on Proper Generalized Decomposition," Mathematics, MDPI, vol. 8(12), pages 1-11, December.
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