Applied Reinforcement Learning for Decision Making in Industrial Simulation Environments

The industrial sector is going through a digital transformation with a high emphasis on Artificial Intelligence-driven operations. In this transformation, the combination between simulation and machine learning has been a key enhancer to provide state-of-the-art solutions to complex systems by training algorithms over virtual representations of industry. In recent years, Reinforcement Learning (RL) has gained traction in this field and shown success in solving sequential decision-making problems. The purpose of this paper is to show how to implement simulation-based Reinforcement Learning. The proposed framework integrates Simio, as a discrete-event simulation environment, and Python, to include the RL algorithm. To demonstrate the applicability of this framework, a job-shop scheduling problem under different scenarios is tested and its results are compared with benchmark heuristic dispatching rules.