Opportunistic maintenance scheduling with deep reinforcement learning

Abstract

The great complexity of advanced manufacturing processes combined with the high investment costs for manufacturing equipment makes the integration of maintenance scheduling a challenging, but similarly crucial task. Opportunistic maintenance scheduling holds the potential to increase the operational performance by considering the opportunity cost of maintenance measures. At the same time, reinforcement learning (RL) has proved to be able to handle complex scheduling tasks. Therefore, RL constitutes a promising approach to develop an integrated maintenance scheduling model to consider order dispatching and maintenance scheduling in a single decision support system. This paper models a real-world use case of a semiconductor front-end wafer fabrication by using discrete-event simulation. In the simulated scenarios, the performance of the integrated dispatching and maintenance scheduling is regulated by both complex novel heuristics adapted to opportunistic maintenance and reinforcement learning. The results show that the RL policy is able to learn a competitive joint scheduling strategy by including internal and external opportunistic opportunities. This indicates that opportunistic maintenance scheduling, with and without RL, holds the potential to improve the performance not only of semiconductor manufacturing but capital-intense machinery industry alike.