{"title":"基于模糊逻辑控制器的遗传算法的机会置换策略优化模型","authors":"S. Haque, A. Kabir, R. Sarker","doi":"10.1109/CEC.2003.1299448","DOIUrl":null,"url":null,"abstract":"The paper presents a genetic algorithm with fuzzy logic controller for determining opportunistic replacement policy for deteriorating components of an equipment or system. An opportunistic replacement model has been formulated by considering the dynamics of the decision process of such a policy. In order to reduce the computational burden involving complete enumeration of all possible policies, genetic algorithm has been used to find near optimal solution by maximizing net benefit to be gained from an opportunistic replacement. A fuzzy logic controller has been used to automatically adjust the fine-tuning structure of genetic algorithm parameters. The performance of the model and the solution procedure has been evaluated for a number of case problems, which clearly demonstrates that the proposed method is very effective.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Optimization model for opportunistic replacement policy using genetic algorithm with fuzzy logic controller\",\"authors\":\"S. Haque, A. Kabir, R. Sarker\",\"doi\":\"10.1109/CEC.2003.1299448\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents a genetic algorithm with fuzzy logic controller for determining opportunistic replacement policy for deteriorating components of an equipment or system. An opportunistic replacement model has been formulated by considering the dynamics of the decision process of such a policy. In order to reduce the computational burden involving complete enumeration of all possible policies, genetic algorithm has been used to find near optimal solution by maximizing net benefit to be gained from an opportunistic replacement. A fuzzy logic controller has been used to automatically adjust the fine-tuning structure of genetic algorithm parameters. The performance of the model and the solution procedure has been evaluated for a number of case problems, which clearly demonstrates that the proposed method is very effective.\",\"PeriodicalId\":416243,\"journal\":{\"name\":\"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2003.1299448\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2003.1299448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization model for opportunistic replacement policy using genetic algorithm with fuzzy logic controller
The paper presents a genetic algorithm with fuzzy logic controller for determining opportunistic replacement policy for deteriorating components of an equipment or system. An opportunistic replacement model has been formulated by considering the dynamics of the decision process of such a policy. In order to reduce the computational burden involving complete enumeration of all possible policies, genetic algorithm has been used to find near optimal solution by maximizing net benefit to be gained from an opportunistic replacement. A fuzzy logic controller has been used to automatically adjust the fine-tuning structure of genetic algorithm parameters. The performance of the model and the solution procedure has been evaluated for a number of case problems, which clearly demonstrates that the proposed method is very effective.