Optimizing Industrial Maintenance Scheduling Through Deep Reinforcement Learning and Simulation Integration

Maintenance scheduling is critical function across numerous industries, where the dynamic complexity of operations often challenge the systems efficiency. This study explores the application of Deep Reinforcement Learning (DRL) to refine scheduling decisions by integrating a simulation tool that replicates an industrial production line. This integration eases the modelling and simulation in real-time of machine operations, job flows, and maintenance activities, capturing the dynamics and complexities of a typical production environment. Our developed tool assesses various maintenance strategies and their direct impacts on productivity, leveraging DRL to enhance decision-making capabilities. We introduce an innovative job-sequencing rule that complements the DRL framework, systematically analysing its effectiveness against traditional heuristic methods. The comparative analysis confirms that our DRL-based approach, coupled with the job sequencing rule, significantly optimises maintenance timing and resource allocation in a flow shop setting. By synthesizing simulation with intelligent algorithms, our method not only optimize maintenance tasks but also boosts overall production efficiency.