{"title":"基于NSGA-II和AMOSA的可重构制造系统工艺计划生成","authors":"A. Bensmaine, L. Benyoucef, M. Dahane","doi":"10.1109/INDIN.2011.6035006","DOIUrl":null,"url":null,"abstract":"Reconfigurable manufacturing system (RMS) is a manufacturing paradigm that cost-effectively responses to market changes. This paper addresses the multi-objective process plan generation problem in RMS. More specifically, two meta-heuristics, namely Non-dominated Sorting Genetic Algorithm (NSGA-II) and Archived Multi-Objective Simulated Annealing (AMOSA), are adapted to generate near-optimal process plans. With the help of a numerical example, the performances of the two metaheuristics are demonstrated and compared.","PeriodicalId":378407,"journal":{"name":"2011 9th IEEE International Conference on Industrial Informatics","volume":"154 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Process plan generation in reconfigurable manufacturing systems using adapted NSGA-II and AMOSA\",\"authors\":\"A. Bensmaine, L. Benyoucef, M. Dahane\",\"doi\":\"10.1109/INDIN.2011.6035006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reconfigurable manufacturing system (RMS) is a manufacturing paradigm that cost-effectively responses to market changes. This paper addresses the multi-objective process plan generation problem in RMS. More specifically, two meta-heuristics, namely Non-dominated Sorting Genetic Algorithm (NSGA-II) and Archived Multi-Objective Simulated Annealing (AMOSA), are adapted to generate near-optimal process plans. With the help of a numerical example, the performances of the two metaheuristics are demonstrated and compared.\",\"PeriodicalId\":378407,\"journal\":{\"name\":\"2011 9th IEEE International Conference on Industrial Informatics\",\"volume\":\"154 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 9th IEEE International Conference on Industrial Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN.2011.6035006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 9th IEEE International Conference on Industrial Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN.2011.6035006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Process plan generation in reconfigurable manufacturing systems using adapted NSGA-II and AMOSA
Reconfigurable manufacturing system (RMS) is a manufacturing paradigm that cost-effectively responses to market changes. This paper addresses the multi-objective process plan generation problem in RMS. More specifically, two meta-heuristics, namely Non-dominated Sorting Genetic Algorithm (NSGA-II) and Archived Multi-Objective Simulated Annealing (AMOSA), are adapted to generate near-optimal process plans. With the help of a numerical example, the performances of the two metaheuristics are demonstrated and compared.
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