Research on flexible job shop scheduling problem with AGV using double DQN

Minghai Yuan (), Liang Zheng, Hanyu Huang, Kaiwen Zhou, Fengque Pei and Wenbin Gu
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Minghai Yuan: Hohai University
Liang Zheng: Hohai University
Hanyu Huang: Hohai University
Kaiwen Zhou: Hohai University
Fengque Pei: Hohai University
Wenbin Gu: Hohai University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 29, 509-535

Abstract: Abstract In the context of Industry 4.0 and intelligent manufacturing, AGVs are widely used in flexible job shop resource transportation, which sharply increases the uncertainty and complexity of the scheduling process. For this reason, an improved double Deep Q Network (DDQN) real-time scheduling method is proposed for the Flexible Job Shop Scheduling Problem with Automated Guided Vehicle (FJSP-AGV) to minimize the makespan. Firstly, the optimization model of the FJSP-AGV is established, and the corresponding constraints and the objective function are defined. Then, the FJSP-AGV is converted into a Markov Decision Process (MDP), in which the state space, action space, and reward function are defined in detail. Next, an improved DDQN is proposed to generate the optimal scheduling policy considering AGV. Finally, the computational experiments are conducted based on data from public benchmarks and the real-world flexible job shop, and the results demonstrate the accuracy and effectiveness of the proposed algorithm.

Keywords: Flexible job shop scheduling; AGV; Double deep Q network; Multi-resources scheduling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02252-8

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