Joint optimization of ordering and maintenance with condition monitoring data

Ramin Moghaddass, Şeyda Ertekin

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

We study a single-unit deteriorating system under condition monitoring for which collected signals are only stochastically related to the actual level of degradation. Failure replacement is costlier than preventive replacement and there is a delay (lead time) between the initiation of the maintenance setup and the actual maintenance, which is closely related to the process of spare parts inventory and/or maintenance setup activities. We develop a dynamic control policy with a two-dimensional decision space, referred to as a warning-replacement policy, which jointly optimizes the replacement time and replacement setup initiation point (maintenance ordering time) using online condition monitoring data. The optimization criterion is the long-run expected average cost per unit of operation time. We develop the optimal structure of such a dynamic policy using a partially observable semi-Markov decision process and provide some important results with respect to optimality and monotone properties of the optimal policy. We also discuss how to find the optimal values of observation/inspection interval and lead time using historical condition monitoring data. Illustrative numerical examples are provided to show thatour joint policy outperforms conventional suboptimal policies commonly used in theliterature.

Original languageEnglish (US)
Pages (from-to)1-40
Number of pages40
JournalAnnals of Operations Research
DOIs
StateAccepted/In press - Jan 4 2018

Keywords

  • Condition monitoring
  • Deteriorating systems
  • Partially observable semi-Markov decision process
  • Real-time analytics

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Management Science and Operations Research

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