Optimizing the flexible job shop scheduling problem via deep reinforcement learning with mean multichannel graph attention

Abstract

Job shop scheduling plays a crucial role in manufacturing informatization. Recently, significant progress has been made in terms of optimizing flexible job shop scheduling problems (FJSPs) via deep reinforcement learning (DRL). However, the complex structures of the disjunctive graphs encountered in FJSPs introduce a large amount of redundant information, and their oversized action spaces further increase the difficulty of training. To address these issues, a mean multichannel graph attention-proximal policy optimization (MCGA-PPO) model is proposed. First, the channel graph attention (CGA) mechanism reduces the amount of redundant information, allowing the agent to focus on task-relevant critical information. Second, for the first time, the overestimation phenomenon observed in FJSPs is explored in depth, and the MCGA method is developed to address the issue of overestimation from a single direction. MCGA employs information weighted across multiple channels to balance the estimation process. Furthermore, to address large action spaces, an entropy loss is introduced to optimize the exploration and exploitation processes of the agent. The experimental results confirm that our proposed model provides performance improvements of 1.22% and 1.29% on synthetic and classic datasets, respectively, demonstrating its effectiveness in addressing complex FJSPs.