4.6 Article

Integrating equipment investment strategy with maintenance operations under uncertain failures

Journal

ANNALS OF OPERATIONS RESEARCH
Volume 317, Issue 2, Pages 353-386

Publisher

SPRINGER
DOI: 10.1007/s10479-015-1862-0

Keywords

Machine maintenance and replacement; Equipment investment; Stopping time; Variational inequality

Funding

  1. Research Grants Council of HKSAR [CityU 500111]
  2. Purdue OVRP Incentive Grant
  3. Krannert GSCMI DeVos Research Award

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This paper investigates the coordination of equipment maintenance operations and capital investment strategy in the presence of random equipment failures. It provides a new approach by explicitly modeling the stochastic process of machine failures. The findings suggest that a deterministic policy is optimal for long-term planning with limited replacement opportunities, while a state-contingent policy must be applied for shorter planning horizons with generous replacement budgets.
This paper studies the issue of coordinating equipment maintenance operations with capital investment strategy in the presence of random equipment failures. The traditional approach, developed by Kamien and Schwartz (KS) in their celebrated paper published in 1971, is to formulate the problem as a deterministic optimal control problem with the probability of machine failure as the state variable. With this approach, the optimal policy is deterministic. As a major departure from the KS approach, we explicitly model the underlying stochastic process of machine failures. Our analysis of the stochastic dynamic programming model offers new insights into the problem. Under a long planning horizon with a limited replacement opportunity, each individual machine serves as a revenue generator and contributes a significant amount to the profit of the system. In contrast, when the replacement budget is quite generous over a relatively short planning horizon, adding one extra machine only helps as a backup for unexpected failures of the machines purchased before it. An interesting result derived from this comparison is that a deterministic policy turns out to be optimal for the former, while a state-contingent policy must be applied to the latter. In other words, the deterministic KS approach does not work in general when a chain of machine replacement is considered. We further characterize the effects of the discount rate, productivity deterioration, learning, decision delay, and technology advancement on the optimal policy.

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