Dynamic job shop scheduling under multiple order disturbances using deep reinforcement learning

Dynamic job shop scheduling problems with multiple order disturbances present significant challenges in manufacturing systems. This paper proposes a novel approach using Independent Proximal Policy Optimization (IPPO), a multiagent deep reinforcement learning algorithm, to address these challenges. We introduce a five-channel two-dimensional image to represent system states and design a reward function that minimizes both total tardiness and makespan. Experimental results across 72 diverse production scenarios demonstrate that our IPPO-based approach outperforms traditional deep reinforcement learning algorithms and dispatching rules in most cases. The proposed method shows strong optimization and exploration capabilities, offering a promising solution for complex, multiobjective scheduling in dynamic manufacturing environments.