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Time-Based Maintenance Models Under Uncertainty

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10740)

Abstract

Model based computation of optimal maintenance strategies is one of the classical applications of Markov Decision Processes. Unfortunately, a Markov Decision Process often does not capture the behavior of a component or system of components correctly because the duration of different operational phases is not exponentially distributed and the status of component is often only partially observable during operational times. The paper presents a general model for components with partially observable states and non-exponential failure, maintenance and repair times which are modeled by phase type distributions. Optimal maintenance strategies are computed using Markov decision theory. However, since the internal state of a component is not completely known, only bounds for the parameters of a Markov decision process can be computed resulting in a bounded parameters Markov decision process. For this kind of process optimal strategies can be computed assuming best, worst or average case behavior.

Keywords

Maintenance models Markov decision processes Stochastic dynamic programming Numerical methods 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Informatik IVTU DortmundDortmundGermany

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