Selective maintenance strategy for systems executing multiple consecutive missions with uncertainty

Selective maintenance is extensively implemented in industrial and military environments. With it, a subset of feasible maintenance actions may be strategically selected for a repairable system to achieve maximum success in subsequent missions with limited maintenance resources. In this study, a new selective maintenance model for systems that execute multiple consecutive missions is developed. In each break, multiple optional maintenance actions, from perfect replacements down to imperfect and minimal repairs, can be chosen for each component. Because of the uncertainties associated with the operation time of each component and the durations of future missions, the effective age of each component at the beginning of the next mission is also uncertain, posing a new challenge in terms of evaluating the success of subsequent missions. Such uncertainties are quantified by evaluating the probabilities of a system in successfully completing future missions. The computational burden resulting from the use of multi-dimensional integrals is alleviated with the introduction of the Gaussian quadrature and Riemann sum. Consequently, the selective maintenance problem is formulated as a max-min optimization model. Moreover, the simulated annealing-based genetic algorithm is customized to solve the resulting optimization problem. Two illustrative examples are presented to demonstrate the advantages of the proposed approach.