A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem

• Published in: Expert systems with applications Vol. 205; p. 117796
• Main Authors: Lei, Kun, Guo, Peng, Zhao, Wenchao, Wang, Yi, Qian, Linmao, Meng, Xiangyin, Tang, Liansheng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022
• Subjects: Flexible job-shop scheduling problem; Graph neural network; Markov decision process; Multi-action deep reinforcement learning; Multi-proximal policy optimization; Flexible job-shop scheduling problem; Markov decision process; Graph neural network; Multi-proximal policy optimization; Multi-action deep reinforcement learning

•An end-to-end DRL-based framework is introduced to solve the FJSP.•Multi-PPO is used to learn job operation action and machine action sub-policies in MPGN.•The proposed DRL shows its robustness via random and benchmark test instances. This paper presents an end-to-end deep reinforcement framework t...