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

Ho, GTS, Tang, Yuk Ming, Leung, Eric KH ORCID: 0000-0003-2058-0287 and Tong, PH (2025) Integrated reinforcement learning of automated guided vehicles dynamic path planning for smart logistics and operations Transportation Research Part E: Logistics and Transportation Review, 196. p. 104008. ISSN 1366-5545

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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.

Item Type:	Article
Uncontrolled Keywords:	3509 Transportation, Logistics and Supply Chains, 35 Commerce, Management, Tourism and Services, 9 Industry, Innovation and Infrastructure
Divisions:	Faculty of Humanities & Social Sciences Faculty of Humanities & Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	17 Feb 2025 08:53
Last Modified:	18 Apr 2025 16:51
DOI:	10.1016/j.tre.2025.104008
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3190333
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