A multiobjective flexible job shop scheduling method based on deep reinforcement learning

Weiqi Liu; Hao Chen; Jianming Zhang; Yaozong Wang

doi:10.1117/12.3014960

4 March 2024 A multiobjective flexible job shop scheduling method based on deep reinforcement learning

Weiqi Liu, Hao Chen, Jianming Zhang, Yaozong Wang

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129814Y (2024) https://doi.org/10.1117/12.3014960
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

To address the multiobjective flexible job shop scheduling problem, this paper establishes a Markov Decision Process for Multiobjective Flexible Job-Shop Scheduling Problem, aiming to minimize makespan and total machine load. Additionally, a multiobjective optimization algorithm base deep reinforcement learning is proposed. Firstly, a multiobjective decomposition strategy is introduced to decompose the problem into a set of subproblems, and graph neural networks with identical structures are employed to represent the priority dispatching rules for each subproblem. Experimental results demonstrate that this approach exhibits better generalization, higher solution quality, and faster convergence compared to previous methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weiqi Liu, Hao Chen, Jianming Zhang, and Yaozong Wang "A multiobjective flexible job shop scheduling method based on deep reinforcement learning", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129814Y (4 March 2024); https://doi.org/10.1117/12.3014960

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