Production rescheduling via explorative reinforcement learning while considering nervousness

Abstract

Nervousness-aware rescheduling is essential in maximizing the profitability and stability of processes in manufacturing industries. It involves re-optimization to meet scheduling goals while minimizing deviations from the base schedule. However, conventional mathematical optimization becomes impractical due to high computational costs and the inability to handle real-time rescheduling. Here, we propose an online rescheduling agent trained by explorative reinforcement learning that autonomously optimizes schedules while considering schedule nervousness. In a static scheduling environment, our model consistently achieves over 90% of the cost objective with scalability and flexibility. A computational time comparison proves that the reinforcement learning methodology makes near-optimal decisions rapidly, irrespective of the complexity of the scheduling problem. Furthermore, we present several realistic rescheduling scenarios that demonstrate the capability of our methodology. Our study illustrates the significant potential of reinforcement learning methodology in expediting digital transformation and process automation within real-world manufacturing systems.