Maintenance Optimization using Combined Fuzzy Genetic Algorithm and Local Search

被引：4

作者：

Maatouk, I ^{[1
]}

Chebbo, N. ^{[1
]}

Jarkass, I ^{[1
]}

Chatelet, E. ^{[2
]}

机构：

[1] Lebanese Univ, Inst Univ Technol, Saida, Lebanon

[2] Univ Technol Troyes, LA12S, UMR CNRS 6281, ICD, Troyes, France

来源：

IFAC PAPERSONLINE | 2016年 / 49卷 / 12期

关键词：

Fuzzy logic; genetic algorithm; local search; optimization; preventive maintenance; SERIES-PARALLEL SYSTEMS; PREVENTIVE MAINTENANCE;

D O I：

10.1016/j.ifacol.2016.07.865

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an hybridization fuzzy logic controlled genetic algorithm, and local search to solve the preventive maintenance optimization problem in a multi-states series-parallel system. The objective is to optimize for each system component the maintenance policy minimizing a cost function under the constraint of required availability and for a specified period. Simulation results are presented for the proposed method which is compared to a genetic algorithm with fixed crossover and mutation probabilities, and to a fuzzy logic controlled genetic algorithm. The experimental results show the advantages and the efficiency of the hybridization FLC-GA, and local search. (C) 2016, IFAC(International Federation of Automatic Control) Hosting by Elsevier Ltd. All right reserved.

引用

页码：757 / 762

页数：6

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