{"title":"Ranking method of the generalized intuitionistic fuzzy numbers founded on possibility measures and its application to MADM problem","authors":"Totan Garai","doi":"10.1007/s43674-023-00061-3","DOIUrl":null,"url":null,"abstract":"<div><p>In the real number set, generalized intuitionistic fuzzy numbers (GIFNs) are an impressive number of fuzzy sets (FSs). GIFNs are very proficient in managing the decision-making problem data. Our aim of this paper is to develop a new ranking method for solving a multi-attribute decision-making (MADM) problem with GIFN data. Here, we have defined the possibility mean and standard deviation of GIFNs. Then, we have formulated the magnitude of membership and non-membership function of GIFNs. In the proposed MADM problem, the attribute values are expressed as GIFNs, which is a very workable environment for decision-making problems. Finally, a numerical example is analyzed to demonstrate the flexibility, applicability and universality of the proposed ranking method and MADM problem.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-023-00061-3.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in computational intelligence","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s43674-023-00061-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the real number set, generalized intuitionistic fuzzy numbers (GIFNs) are an impressive number of fuzzy sets (FSs). GIFNs are very proficient in managing the decision-making problem data. Our aim of this paper is to develop a new ranking method for solving a multi-attribute decision-making (MADM) problem with GIFN data. Here, we have defined the possibility mean and standard deviation of GIFNs. Then, we have formulated the magnitude of membership and non-membership function of GIFNs. In the proposed MADM problem, the attribute values are expressed as GIFNs, which is a very workable environment for decision-making problems. Finally, a numerical example is analyzed to demonstrate the flexibility, applicability and universality of the proposed ranking method and MADM problem.
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