{"title":"Using PCA and Pareto optimality to select flexible manufacturing systems","authors":"K. Rezaie, A. Haeri","doi":"10.1109/SYSCON.2011.5929062","DOIUrl":null,"url":null,"abstract":"Flexible manufacturing systems (FMS) attract more attention in recent years. Design and establishing of a FMS need much investment. So it is necessary to make best decision for selection of a FMS alternative. In this paper an approach for selecting a FMS alternative on the basis of the performance criteria is presented. Six input and output factors are used for FMS selection. Initially the approach use Principle Component Analysis (PCA) for decreasing number of criteria. After performing PCA two criteria are generated instead of initial six criteria. After that Pareto optimality and Pareto front concepts are used for rank FMS alternatives on the basis of the two new criteria. The proposed approach is performed on a data set that contains specification of 12 FMS alternatives. At the end suggestions for future research is mentioned.","PeriodicalId":109868,"journal":{"name":"2011 IEEE International Systems Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYSCON.2011.5929062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Flexible manufacturing systems (FMS) attract more attention in recent years. Design and establishing of a FMS need much investment. So it is necessary to make best decision for selection of a FMS alternative. In this paper an approach for selecting a FMS alternative on the basis of the performance criteria is presented. Six input and output factors are used for FMS selection. Initially the approach use Principle Component Analysis (PCA) for decreasing number of criteria. After performing PCA two criteria are generated instead of initial six criteria. After that Pareto optimality and Pareto front concepts are used for rank FMS alternatives on the basis of the two new criteria. The proposed approach is performed on a data set that contains specification of 12 FMS alternatives. At the end suggestions for future research is mentioned.