Deep Reinforcement Learning Using Multiview Graph Attention Mechanisms for Flexible Job Shop Scheduling

Abstract

The flexible job shop scheduling problem (FJSP) is a typical challenge in modern manufacturing system scheduling. Classical optimization methods encounter difficulties extracting complex structural information in disjunctive graphs. An end-to-end approach is proposed in which a graph attention network (GAT) is integrated with deep reinforcement learning (DRL) to address the FJSP. The FJSP is modeled as a multi-Markov decision process (MMDP), where operation selection and machine assignment are treated as separate action spaces. A multiview GAT (MGAT) is introduced to mine the relationships between operations and machines in the disjunctive graph via two interconnected graph attention modules that extract structural information to improve the dynamic decision-making ability of the policy network. The probability distributions for operation selection and machine assignment are predicted by the designed policy networks. The policy networks are trained using the multiproximal policy optimization (multi-PPO) algorithm, and the resulting policies are subsequently employed to solve FJSP instances across various scales. Experiments on benchmark instances demonstrate that the proposed method outperforms heuristic scheduling rules and the heterogeneous graph neural network (HGNN) algorithm.