Integrated production and maintenance planning under uncertain demand with concurrent learning of yield rate

Strong interactions between decisions in the maintenance and production scheduling domains, and their impacts on the equipment yield rates necessitate maintenance and production decisions being optimized concurrently, with considerations of yield dependencies on the equipment conditions and production rates. This paper proposes an integrated decision-making policy for production and maintenance operations on a single machine under uncertain demand, with concurrent considerations and learning of yield dependencies on the equipment conditions and production rates. This policy is obtained through a two-stage stochastic programming model, which considers the variable demand, machine degradation, and maintenance times. This model incorporates outsourcing decisions and operational decisions regarding reworking, scraping of imperfect products to ensure the demand is adequately met. A closed-form reinforcement learning method is utilized to learn yield dependencies. Simulations confirm the necessity of yield learning and show the proposed method outperforms the traditional, fragmented approaches where the effects of production rates and machine conditions on the resulting yield rates are not considered. The two-stage stochastic setting is demonstrated by comparing with the traditional one-stage deterministic approach, where decisions are made based on the expected demand and production performance, with scrapping, reworking, and outsourcing decisions established before the demand and production performance are observed.