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A simple suboptimal algorithm for system maintenance under partial observability

delete1999-01-01
delete4
PRE
AI
D
David Isern *
L
Leonard Friedman
Z
Zilla Sinuany‐Stern
DOI：10.1023/A:1018949723461delete
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Abstract

Abstract

En 中文
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.
Keywords:
partially observed Markov decision process
algorithms
suboptimal design
dynamic programming
Bayesian programming
reliability/maintenance
machine replacement
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Journal

Annals of Operations Research cover
Annals of Operations Research
IF:
4.5
Papers:
8.0K
Citations:
2.1W

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No organization information available