Maintaining a system subject to uncertain technological evolution

• Published in: Reliability engineering & system safety Vol. 128; no. n128; pp. 56 - 65
• Main Authors: Nguyen, T.P.K., Castanier, Bruno, Yeung, Thomas G.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.08.2014; Elsevier
• Subjects: Applied sciences; Computer Science; Decision theory. Utility theory; Decisions; Dynamic programming; Exact sciences and technology; Financing; Forecast horizon; Horizon; Investment; Maintenance; Maintenance/replacement investment; Markets; Markov decision processes; Markov processes; Mathematical models; Mathematics; Modeling and Simulation; Operational research and scientific management; Operational research. Management science; Optimization; Portfolio theory; Probability and statistics; Probability theory and stochastic processes; Reliability theory. Replacement problems; Sciences and techniques of general use; Technology change; Forecast horizon; Dynamic programming; Markov decision processes; Technology change; Maintenance/replacement investment; Markov decision; Replacement problem; Optimal policy; Income; Maintenance cost; Non stationary process; Modeling; Optimization; Uncertain system; Economy; Profit; Robustness; Purchases; Infinite horizon; Repair; Mathematical programming; Probabilistic approach; Decision making; Markets; Replacement; System maintenance; Technological change; Investment; MAINTENANCE; INVESTISSEMENT; ANALYSE DE SYSTEMES
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2014.04.004

Maintenance decisions can be directly affected by the introduction of a new asset on the market, especially when the new asset technology could increase the expected profit. However new technology has a high degree of uncertainty that must be considered such as, e.g., its appearance time on the market, the expected revenue and the purchase cost. In this way, maintenance optimization can be seen as an investment problem where the repair decision is an option for postponing a replacement decision in order to wait for a potential new asset. Technology investment decisions are usually based primarily on strategic parameters such as current probability and expected future benefits while maintenance decisions are based on “functional” parameters such as deterioration levels of the current system and associated maintenance costs. In this paper, we formulate a new combined mathematical optimization framework for taking into account both maintenance and replacement decisions when the new asset is subject to technological improvement. The decision problem is modelled as a non-stationary Markov decision process. Structural properties of the optimal policy and forecast horizon length are then derived in order to guarantee decision optimality and robustness over the infinite horizon. Finally, the performance of our model is highlighted through numerical examples.