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
收藏
页码：757 / 762
页数：6
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