Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems

Production scheduling is a critical task in the management of manufacturing systems. It is difficult to derive an optimal schedule due to the problem complexity. Computationally expensive and time-consuming solutions have created major issues for companies trying to respect their customers’ demands. Simple dispatching rules have typically been applied in manufacturing practice and serve as a good scheduling option, especially for small and midsize enterprises (SMEs). However, in recent years, the progress in smart systems enabled by artificial intelligence (AI) and machine learning (ML) solutions has revolutionized the scheduling approach. Under different production circumstances, one dispatch rule may perform better than others, and expert knowledge is required to determine which rule to choose. The objective of this work is to design and implement a framework for the modeling and deployment of a deep reinforcement learning (DRL) agent to support short-term production scheduling. The DRL agent selects a dispatching rule to assign jobs to manufacturing resources. The model is trained, tested and evaluated using a discrete event simulation (DES) model that simulates a pilot case from the bicycle production industry. The DRL agent can learn the best dispatching policy, resulting in schedules with the best possible production makespan.