Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System

In this research, different optimization models are developed to solve the preventive maintenance (PM) optimization problem in a maintainable multi-state series–parallel system. The objective is to determine for each component in the system the maintenance period minimizing a cost function under the...

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Main Authors: Maatouk Imane, Jarkass Iman, Châtelet Eric, Chebbo Nazir
Format: Article
Language:English
Published: De Gruyter 2019-04-01
Series:Journal of Intelligent Systems
Subjects:
Online Access:https://doi.org/10.1515/jisys-2017-0096
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spelling doaj-09562a3d6d4c4907b4f28887cbab8d7a2021-09-06T19:40:38ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2019-04-0128221923010.1515/jisys-2017-0096Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State SystemMaatouk Imane0Jarkass Iman1Châtelet Eric2Chebbo Nazir3Institut Universitaire de Technologie, Lebanese University, Saida, LebanonInstitut Universitaire de Technologie, Lebanese University, Saida, LebanonUniversité de Technologie de Troyes (UTT), CNRS, Institut Charles Delaunay (ICD/LM2S), Troyes,France, chatelet@utt.frInstitut Universitaire de Technologie, Lebanese University, Saida, LebanonIn this research, different optimization models are developed to solve the preventive maintenance (PM) optimization problem in a maintainable multi-state series–parallel system. The objective is to determine for each component in the system the maintenance period minimizing a cost function under the constraint of required availability and for a specified horizon of time. Four genetic models based on the cost associated with maintenance schedule and availability characteristic parameters are constructed and analyzed. They are genetic algorithm (GA), hybridization GA and local search (GA-LS), fuzzy logic controlled GA (FLC-GA), and hybridization FLC-GA and LS. The experiment analyzes and compares the efficiency between them. These experiments investigate the effect of the parameters of the GA on the structure of optimal PM schedules in multi-state multi-component series–parallel systems. Results show that the hybridization FLC-GA and LS outperform the other algorithms.https://doi.org/10.1515/jisys-2017-0096fuzzy logicgenetic algorithmlocal searchoptimizationpreventive maintenance
collection DOAJ
language English
format Article
sources DOAJ
author Maatouk Imane
Jarkass Iman
Châtelet Eric
Chebbo Nazir
spellingShingle Maatouk Imane
Jarkass Iman
Châtelet Eric
Chebbo Nazir
Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System
Journal of Intelligent Systems
fuzzy logic
genetic algorithm
local search
optimization
preventive maintenance
author_facet Maatouk Imane
Jarkass Iman
Châtelet Eric
Chebbo Nazir
author_sort Maatouk Imane
title Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System
title_short Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System
title_full Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System
title_fullStr Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System
title_full_unstemmed Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System
title_sort preventive maintenance optimization and comparison of genetic algorithm models in a series–parallel multi-state system
publisher De Gruyter
series Journal of Intelligent Systems
issn 0334-1860
2191-026X
publishDate 2019-04-01
description In this research, different optimization models are developed to solve the preventive maintenance (PM) optimization problem in a maintainable multi-state series–parallel system. The objective is to determine for each component in the system the maintenance period minimizing a cost function under the constraint of required availability and for a specified horizon of time. Four genetic models based on the cost associated with maintenance schedule and availability characteristic parameters are constructed and analyzed. They are genetic algorithm (GA), hybridization GA and local search (GA-LS), fuzzy logic controlled GA (FLC-GA), and hybridization FLC-GA and LS. The experiment analyzes and compares the efficiency between them. These experiments investigate the effect of the parameters of the GA on the structure of optimal PM schedules in multi-state multi-component series–parallel systems. Results show that the hybridization FLC-GA and LS outperform the other algorithms.
topic fuzzy logic
genetic algorithm
local search
optimization
preventive maintenance
url https://doi.org/10.1515/jisys-2017-0096
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AT chateleteric preventivemaintenanceoptimizationandcomparisonofgeneticalgorithmmodelsinaseriesparallelmultistatesystem
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