Multicomponent Maintenance Optimization: A Stochastic Programming Approach

Maintenance optimization has been extensively studied in the past decades. However, most of existing maintenance models focus on single-component systems and are not applicable to complex consisting multiple components, due various interactions among components. The multicomponent problem, which joins stochastic processes regarding failures components with combinatorial problems grouping activities, is challenging both modeling solution techniques, remained an open issue literature. In this paper, we study problem over a finite planning horizon formulate as multistage integer program decision-dependent uncertainty. There lack general efficient methods solve type problem. To address challenge, use alternative approach model underlying failure process develop novel two-stage without Structural properties investigated, progressive-hedging-based heuristic developed based structural properties. Our algorithm demonstrates significantly improved capacity handle large-size comparing three conventional for programming, solving by our rolling provides good approximation further benchmarked dynamic programming policy, two commonly adopted approaches Numerical results show that can lead significant cost savings compared benchmark approaches.