Unified DRL for Enhanced Flexible Job-Shop Scheduling with Transportation Constraints

In the domain of smart manufacturing systems (SMSs),the flexible job-shop scheduling problem with transportation constraints (FJSPT) represents a crucial challenge and holds the potential to significantly enhance production efficiency. Extending beyond the traditional job-shop scheduling problem (JSP), FJSPT incorporates the scheduling of automated guided vehicles (AGVs) to transport intermediate products between machines. Despite the promise of deep reinforcement learning (DRL) in solving combinatorial optimization problems, there remains a notable scarcity of research employing DRL to tackle FJSPT. To address this gap, this paper introduces an end-to-end DRL approach for simultaneous scheduling of machines and AGVs in FJSPT. The proposed method amalgamates operation selection, machine assignment, and AGV planning into a unified decision-making process, leveraging a graph attention network (GAT) and the proximal policy optimization (PPO) algorithm for efficient and stable training. Experimental evaluations conducted on synthetic data and public instances demonstrate that the proposed method outperforms existing approaches in terms of scheduling performance and computational efficiency.