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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Online Access: | https://doi.org/10.1515/jisys-2017-0096 |
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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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