Dynamic Job-Shop Scheduling via Graph Attention Networks and Deep Reinforcement Learning

The dynamic job-shop scheduling problem (DJSSP) is an advanced form of the classical job-shop scheduling problem (JSSP), incorporating dynamic events that make it even more challenging. This article proposes a novel approach involving deep reinforcement learning and graph neural networks to solve this optimization problem. To effectively model DJSSP, we use a disjunctive graph, designing specific node features that reflect the unique characteristics of JSSP with machine breakdowns and stochastic job arrivals. Our proposed method can dynamically adapt to the occurrence of disruptions, ensuring that it accurately reflects the current state of the environment. Furthermore, we use the attention mechanism to prioritize crucial nodes while discarding irrelevant ones. This study proposes a model that applies graph attention networks to learn node embeddings, serving as input for the actor-critic model. The proximal policy optimization is then utilized to train the actor-critic model, which assists the model in learning the scheduling of job operations for machines. We conducted extensive experiments in static and public environments. Experimental results indicate that our method is superior to current state-of-the-art methods.