Quantitative assessments of performance and robustness of maintenance policies for stochastically deteriorating production systems

Over the last few decades, many efforts have been invested in improving the economic performances of maintenance policies for stochastically deteriorating production systems. However, with the development of complex production systems, maintenance managers are interested not only in cost saving, but also in how to trustworthily plan and allocate the required maintenance budget. In this context, the robustness of maintenance policies which is related to the maintenance cost variability from a renewal cycle to another plays a pivotal role. This research deals with a quantitative approach to jointly assess the economic performance and robustness of some representatives of two most well-known classes of maintenance policies: time-based and condition-based maintenance. To this end, we first propose a new cost criterion which combines the long-run expected cost rate and standard deviation of maintenance cost per renewal cycle. Then, we develop and compare the associated mathematical cost models of the considered maintenance policies on the basis of the Gamma degradation process and the theory of stochastic renewal processes. The comparison results under different situations of maintenance costs and system characteristics show that the optimal configuration of maintenance policies gives the best compromise between the performance and robustness, and is mostly affected by the system downtime. Under this aspect, the condition-based maintenance remains more profitable than the time-based maintenance. Still, maintenance managers could implement condition-based maintenance policies that efficiently control the downtime to maximise the maintenance effectiveness of production systems from both performance and robustness viewpoints.