Reinforcement learning for scheduling of maintenance

Abstract

Improving maintenance scheduling has become an area of crucial importance in recent years. Condition-based maintenance (CBM) has started to move away from scheduled maintenance by providing an indication of the likelihood of failure. Improving the timing of maintenance based on this information to maintain high reliability without resorting to over-maintenance remains, however, a problem. In this paper we propose Reinforcement Learning (RL), to improve long term reward for a multistage decision based on feedback given either during or at the end of a sequence of actions, as a potential solution to this problem. Several indicative scenarios are presented and simulated experiments illustrate the performance of RL in this application. © 2011 Springer-Verlag London Limited.