Reinforcement learning for combined production-maintenance and quality control of a manufacturing system with deterioration failures

This paper describes and examines thoroughly a stochastic production/inventory system that produces a single type of products. During the production process, the system is affected by several deterioration failures. It is restored to its initial and previous deterioration state by repair and maintenance activities. Both maintenance and repair duration are assumed as exponential random variables. Moreover, the quality of the manufactured products is assumed to be affected by the current deterioration level of the system. The aim of this paper is to find the optimal trade-off between conflicting performance metrics for the optimization of the total expected profit of the system. To tackle such optimization problems, researchers frequently employ Dynamic Programming. This method, though, is not appropriate for the addressed problem due to complexity reasons. To this end, a Reinforcement Learning-based approach is proposed in order to obtain the optimal joint production, maintenance and product quality control policies. To the authors' knowledge, the proposed approach is novel and there are few examples of such implementation in the academic literature.