Maintenance optimization for a Markovian deteriorating system with population heterogeneity

Chiel Van Oosterom*, Hao Peng, Geert Jan Van Houtum

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)


We develop a partially observable Markov decision process model to incorporate population heterogeneity when scheduling replacements for a deteriorating system. The single-component system deteriorates over a finite set of condition states according to a Markov chain. The population of spare components that is available for replacements is composed of multiple component types that cannot be distinguished by their exterior appearance but deteriorate according to different transition probability matrices. This situation may arise, for example, because of variations in the production process of components. We provide a set of conditions for which we characterize the structure of the optimal policy that minimizes the total expected discounted operating and replacement cost over an infinite horizon. In a numerical experiment, we benchmark the optimal policy against a heuristic policy that neglects population heterogeneity.

Original languageEnglish
Pages (from-to)96-109
Number of pages14
JournalIISE Transactions
Issue number1
Publication statusPublished - 2017
Externally publishedYes


  • Optimal policy structure
  • Partially observable markov decision process
  • Population heterogeneity
  • Replacement optimization

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