Optimizing Job Shop Scheduling Problem Through Deep Reinforcement Learning and Discrete Event Simulation

This paper explores the optimization of Job Shop Scheduling Problem (JSSP), by employing a Deep Reinforcement Learning (DRL) approach that learns optimal scheduling policies through continuous inter-action with a job shop setting simulated within a discrete event simulation (DES) environment. The study involves computational experiments to train and evaluate the DRL agent across benchmark JSSP instances. We compare our DRL-based scheduling solutions against generated by widely used priority dispatching rules, assessing the impact of the learned policies on performance metrics including the total time to complete all jobs (makespan) and efficiency in using machines (machine utilization). Our results indicate improvements in scheduling efficiency, showcasing the DRL algorithm's ability to adapt and optimize in complex scheduling scenarios. This paper underscores the potential of integrating DRL with DES to create a powerful toolset for modern manufacturing challenges, enabling businesses to maintain competitive advantage through improved operational agility.