A simple suboptimal algorithm for system maintenance under partial observability

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4 Scopus citations

Abstract

We suggest a heuristic solution procedure for Partially Observable Markov Decision Processes with finite action space and finite state space with infinite horizon. The algorithm is a fast, very simple general heuristic; it is applicable for multiple states (not necessarily ordered) multiple actions and various distribution functions. The quality of the algorithm is checked in this paper against existing analytical and empirical results for two specific models of machine replacement. One model refers to the case of two-action and two-system states with uniform observations (Grosfeld-Nir [4]), and the other model refers to a case of many ordered states with binomial observations (Sinuany-Stern et al. [11]). The paper also presents the model realization for various probability distribution functions applied to maintenance and quality control.

Original languageEnglish
Pages (from-to)25-40
Number of pages16
JournalAnnals of Operations Research
Volume91
DOIs
StatePublished - 1 Jan 1999

Keywords

  • Dynamic programming: Bayesian programming
  • Partially observed Markov decision process: algorithms
  • Reliability/maintenance: machine replacement
  • Suboptimal design

ASJC Scopus subject areas

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

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