Optimization Scheduling of Dynamic Industrial Systems Based on Reinforcement Learning

The flexible job shop scheduling problem (FJSP) is a fundamental challenge in modern industrial manufacturing, where efficient scheduling is critical for optimizing both resource utilization and overall productivity. Traditional heuristic algorithms have been widely used to solve the FJSP, but they are often tailored to specific scenarios and struggle to cope with the dynamic and complex nature of real-world manufacturing environments. Although deep learning approaches have been proposed recently, they typically require extensive feature engineering, lack interpretability, and fail to generalize well under unforeseen disturbances such as machine failures or order changes. To overcome these limitations, we introduce a novel hierarchical reinforcement learning (HRL) framework for FJSP, which decomposes the scheduling task into high-level strategic decisions and low-level task allocations. This hierarchical structure allows for more efficient learning and decision-making. By leveraging policy gradient methods at both levels, our approach learns adaptive scheduling policies directly from raw system states, eliminating the need for manual feature extraction. Our HRL-based method enables real-time, autonomous decision-making that adapts to changing production conditions. Experimental results show our approach achieves a cumulative reward of 199.50 for Brandimarte, 2521.17 for Dauzère, and 2781.56 for Taillard, with success rates of 25.00%, 12.30%, and 19.00%, respectively, demonstrating the robustness of our approach in real-world job shop scheduling tasks.