Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation

Abstract

Scheduling in the process industry is a highly demanding task. Having access to optimal production schedules at short notice, for instance, after spontaneous changes, offers numerous advantages in terms of robustness, economics, and ultimately customer satisfaction, as delays are minimized. In this work, we describe our initial efforts to apply and evaluate deep reinforcement learning (DRL) for optimized scheduling in a typical fill-and-finish batch production plant in the chemical industry. Our pilot study demonstrates how DRL can be implemented using an approach based on discrete event simulation. We discuss the results and benefits of DRL, compare it to mathematical programming approaches, and outline a potential path forward. Our study suggests that the application of DRL in the chemical industry is a promising research direction and that DRL can complement established methods such as process simulation and mathematical programming.

AI in Process Industries – Current Status and Future Prospects

“…Production scheduling is another activity in process operations where operator support can lead to large improvements of the productivity and of the energy consumption of production plants. The contribution [56] describes the application of reinforcement learning to this problem.…”