{"title":"Continuous probability-interval valued fuzzy preference relations and its application in group decision making","authors":"J. Mo, H. L. Huang","doi":"10.22111/IJFS.2021.6184","DOIUrl":null,"url":null,"abstract":"Probabilistic hesitant fuzzy set represents the occurrence probabilities of elements.The probabilistic hesitant fuzzy preference relations can more effectively express thehesitant preference information of decision makers.But in the existing research, all of them are based on discrete probability distribution.In order to give decision maker more evaluation space,continuous probability distribution is necessary to be considered.Therefore, in this paper, the continuous probability-interval valued fuzzy setis defined and its probability is represented by a probability density function.A method of converting probabilistic hesitant fuzzy set into continuous probability-interval valued fuzzy setis developed to transform discrete data into continuous data.Then, the continuous probability-interval valued fuzzy preference relations is presented.In order to consider the consistency of continuous probability-interval valued fuzzy preference relations, the multiplication consistent expected preference relations is proposed.The individual consistency index and group consensus index are also presented to determine the consistency level.And then, an algorithm is introduced for checking and improving the individual consistency level andgroup consensus level.Finally, a numerical example is shown to the effectiveness of proposed algorithm,the comparative analysis is given with the existing methods toshow the superiority of this algorithm.","PeriodicalId":54920,"journal":{"name":"Iranian Journal of Fuzzy Systems","volume":"69 1","pages":"185-200"},"PeriodicalIF":1.9000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iranian Journal of Fuzzy Systems","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.22111/IJFS.2021.6184","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Probabilistic hesitant fuzzy set represents the occurrence probabilities of elements.The probabilistic hesitant fuzzy preference relations can more effectively express thehesitant preference information of decision makers.But in the existing research, all of them are based on discrete probability distribution.In order to give decision maker more evaluation space,continuous probability distribution is necessary to be considered.Therefore, in this paper, the continuous probability-interval valued fuzzy setis defined and its probability is represented by a probability density function.A method of converting probabilistic hesitant fuzzy set into continuous probability-interval valued fuzzy setis developed to transform discrete data into continuous data.Then, the continuous probability-interval valued fuzzy preference relations is presented.In order to consider the consistency of continuous probability-interval valued fuzzy preference relations, the multiplication consistent expected preference relations is proposed.The individual consistency index and group consensus index are also presented to determine the consistency level.And then, an algorithm is introduced for checking and improving the individual consistency level andgroup consensus level.Finally, a numerical example is shown to the effectiveness of proposed algorithm,the comparative analysis is given with the existing methods toshow the superiority of this algorithm.
期刊介绍:
The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling.
Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.