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Joint Optimization of Sampling and Control of Partially Observable Failing Systems

Michael Jong Kim () and Viliam Makis ()
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Michael Jong Kim: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada; and Department of Decision Sciences, NUS Business School, Singapore, Republic of Singapore
Viliam Makis: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada

Operations Research, 2013, vol. 61, issue 3, 777-790

Abstract: Stochastic control problems that arise in reliability and maintenance optimization typically assume that information used for decision-making is obtained according to a predetermined sampling schedule. In many real applications, however, there is a high sampling cost associated with collecting such data. It is therefore of equal importance to determine when information should be collected and to decide how this information should be utilized for maintenance decision-making. This type of joint optimization has been a long-standing problem in the operations research and maintenance optimization literature, and very few results regarding the structure of the optimal sampling and maintenance policy have been published. In this paper, we formulate and analyze the joint optimization of sampling and maintenance decision-making in the partially observable Markov decision process framework. We prove the optimality of a policy that is characterized by three critical thresholds, which have practical interpretation and give new insight into the value of condition-based maintenance programs in life-cycle asset management. Illustrative numerical comparisons are provided that show substantial cost savings over existing suboptimal policies.

Keywords: reliability; maintenance/repairs; inspection; failure models; dynamic programming/optimal control; Markov; applications (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (27)

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