A Novel Heterogeneous Graph Attention‐Enhanced Deep Reinforcement Learning‐Based Framework for Production Scheduling in Cloud Manufacturing

Cloud‐edge collaborative manufacturing supports cloud‐based training and edge‐side execution, requiring production scheduling policies with both rapid response and strong generalization. For the flexible job shop scheduling problem, existing deep reinforcement learning (DRL) methods usually rely on priority dispatching rules which limit generalization performance. To address this issue, this paper proposes heterogeneous graph attention‐enhanced DRL‐based scheduling (HGA‐DS), a heterogeneous graph attention‐enhanced deep reinforcement learning framework, and adopts dynamic joint operation‐machine selection for scheduling decisions. Specifically, an extended heterogeneous graph with job nodes is introduced to improve state representation, an estimated maximum finish time based reward function is designed to provide more effective training feedback, and a lightweight state‐conditioned attention mechanism is developed for low‐overhead neighbourhood information aggregation. Experimental results on datasets of different scales show that HGA‐DS outperforms existing learning‐based methods and composite dispatching rules, while exhibiting strong generalization ability and real‐time decision‐making performance.