{"title":"Projection method for multiple attribute decision making with uncertain attribute weights under intuitionistic fuzzy environment","authors":"Yujun Luo","doi":"10.1109/CCDC.2009.5191817","DOIUrl":null,"url":null,"abstract":"The multiple attribute decision making problems with incomplete attribute weights under intuitionistic fuzzy environment are investigated. Some basic concepts related to the theory of intuitionistic fuzzy set, including the intuitionistic fuzzy weighted averaging (IFWA) operator, score function, and accuracy function, are reviewed. Based on the concept of projection, some optimization models, by which the attribute weights can be derived, are established. Then the alternatives are ranked, and the most desirable one is selected according to the score and accuracy degree of the collective intuitionistic fuzzy attribute values aggregated by IFWA operator. Finally, the feasibility of the proposed approach is verified by an illustrative example.","PeriodicalId":127110,"journal":{"name":"2009 Chinese Control and Decision Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Chinese Control and Decision Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2009.5191817","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The multiple attribute decision making problems with incomplete attribute weights under intuitionistic fuzzy environment are investigated. Some basic concepts related to the theory of intuitionistic fuzzy set, including the intuitionistic fuzzy weighted averaging (IFWA) operator, score function, and accuracy function, are reviewed. Based on the concept of projection, some optimization models, by which the attribute weights can be derived, are established. Then the alternatives are ranked, and the most desirable one is selected according to the score and accuracy degree of the collective intuitionistic fuzzy attribute values aggregated by IFWA operator. Finally, the feasibility of the proposed approach is verified by an illustrative example.
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