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Article . 2020 . Peer-reviewed
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Article . 2020
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Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems

Application of Markov renewal theory and semi-Markov decision processes in maintenance modeling and optimization of multi-unit systems
Authors: Nooshin Salari; Viliam Makis;

Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems

Abstract

AbstractIn this paper, a condition‐based maintenance model for a multi‐unit production system is proposed and analyzed using Markov renewal theory. The units of the system are subject to gradual deterioration, and the gradual deterioration process of each unit is described by a three‐state continuous time homogeneous Markov chain with two working states and a failure state. The production rate of the system is influenced by the deterioration process and the demand is constant. The states of the units are observable through regular inspections and the decision to perform maintenance depends on the number of units in each state. The objective is to obtain the steady‐state characteristics and the formula for the long‐run average cost for the controlled system. The optimal policy is obtained using a dynamic programming algorithm. The result is validated using a semi‐Markov decision process formulation and the policy iteration algorithm. Moreover, an analytical expression is obtained for the calculation of the mean time to initiate maintenance using the first passage time theory.

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Keywords

Reliability, availability, maintenance, inspection in operations research, Markov and semi-Markov decision processes, Applications of Markov renewal processes (reliability, queueing networks, etc.), multi-unit system, stochastic dynamic programming, Dynamic programming, Markov renewal theory, maintenance

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Top 10%
Average
Top 10%
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