Multi-component Maintenance Optimization: A Stochastic Programming Approach
| Autor: | Zhu, Zhicheng, Xiang, Yisha, Zeng, Bo |
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| Rok vydání: | 2019 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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 for complex systems consisting of multiple components, due to various interactions between the components. Multi-component maintenance optimization problem, which joins the stochastic processes regarding the failures of the components with the combinatorial problems regarding the grouping of maintenance activities, is challenging in both modeling and solution techniques, and has remained as an open issue in the literature. In this paper, we study the multi-component maintenance problem over a finite planning horizon and formulate the problem as a multi-stage 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 in handling practically 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 multi-stage problem. The heuristic is further benchmarked with a dynamic programming approach commonly adopted in the literature. Numerical results show that our heuristic can lead to significant cost savings compared with the benchmark approach. Comment: under 2nd round revision at INFORMS Journal on Computing |
| Databáze: | arXiv |
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