Multicomponent Maintenance Optimization: A Stochastic Programming Approach

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

Automated summary

Maintenance optimization for complex systems consisting of multiple components has been a challenging and open issue in the literature. In this paper, a novel two-stage model and a progressive-hedging-based heuristic algorithm were developed to address this challenge. Numerical results show that the heuristic algorithm can lead to significant cost savings compared to benchmark approaches such as dynamic programming and structural policy.