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Fusion Q-Learning Algorithm for Open Shop Scheduling Problem with AGVs

Author

Listed:
  • Xiaoyu Wen

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Haobo Zhang

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Hao Li

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Haoqi Wang

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Wuyi Ming

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
    Guangdong Intelligent Engineering Technology Research Center for Manufacturing Equipment & Guangdong HUST Industrial Technology Research Institute, Huazhong University of Science and Technology, Dongguan 523770, China)

  • Yuyan Zhang

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Like Zhang

    (Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

Abstract

In accordance with the actual production circumstances of enterprises, a scheduling problem model is designed for open-shop environments, considering AGV transport time. A Q-learning-based method is proposed for the resolution of such problems. Based on the characteristics of the problem, a hybrid encoding approach combining process encoding and AGV encoding is applied. Three pairs of actions are constituted to form the action space. Decay factors and a greedy strategy are utilized to perturb the decision-making of the intelligent agent, preventing it from falling into local optima while simultaneously facilitating extensive exploration of the solution space. Finally, the proposed method proved to be effective in solving the open-shop scheduling problem considering AGV transport time through multiple comparative experiments.

Suggested Citation

  • Xiaoyu Wen & Haobo Zhang & Hao Li & Haoqi Wang & Wuyi Ming & Yuyan Zhang & Like Zhang, 2024. "Fusion Q-Learning Algorithm for Open Shop Scheduling Problem with AGVs," Mathematics, MDPI, vol. 12(3), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:452-:d:1330105
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