Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation

Scheduling in the process industry is a highly demanding task. Having access to optimal production schedules at short notice has many advantages regarding robustness, economics and in the end serves the customer, as delays are minimized. In this work we describe how we start applying and evaluating deep reinforcement learning for optimal scheduling in a typical fill and finish production plant.

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.

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