A maintenance optimization and post-decision performance assessment framework of a production system under dynamic environments and maintenance delays

In this study, we consider the maintenance optimization and performance assessment of a production system consisting of multiple deteriorating subsystems. The system operating environment is dynamic and has an impact on the system productivity, system deterioration and the maintenance accessibility. The objective of this work is twofold. First, we intend to minimize the long-run expected discounted production and maintenance costs over an infinite horizon. The problem is cast into a Markov decision process framework. Some structural properties of the optimal maintenance policy are derived. Secondly, the system performance in the post-maintenance scenario is assessed. By utilizing the Markov renewal theorem, we propose some reliability and production indicators, such as the maintenance rate, system availability, production rate, etc. to thoroughly assess the system performance. Numerical experiments are conducted to show the applicability of the proposed model. The advantages of the proposed model regarding the considered expected cost and the system performance are examined through policy comparisons.