{"title":"基于三参数区间灰数的灰色决策模型","authors":"Xiaolu Li, Weiming Yang, Bing-jun Li","doi":"10.1109/GSIS.2017.8077704","DOIUrl":null,"url":null,"abstract":"For the multi-index decision problem with uncertain information, this paper introduces the definition of interval distance of three-parameter interval grey number, proposes the relative degree of grey incidence based on interval distance of three-parameter interval grey number, constructs the grey incidence decision-making model with three-parameter interval grey number, measures the relative degree of grey incidence based on interval distance, and sorts the decision schemes according to the relative degree of grey incidence to select the best scheme. Finally, this model is applied to a practical decision-making problem, and the feasibility of the method is proved by comparing with the result of the existing example.","PeriodicalId":425920,"journal":{"name":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Grey decision model based on three-parameter interval grey number\",\"authors\":\"Xiaolu Li, Weiming Yang, Bing-jun Li\",\"doi\":\"10.1109/GSIS.2017.8077704\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the multi-index decision problem with uncertain information, this paper introduces the definition of interval distance of three-parameter interval grey number, proposes the relative degree of grey incidence based on interval distance of three-parameter interval grey number, constructs the grey incidence decision-making model with three-parameter interval grey number, measures the relative degree of grey incidence based on interval distance, and sorts the decision schemes according to the relative degree of grey incidence to select the best scheme. Finally, this model is applied to a practical decision-making problem, and the feasibility of the method is proved by comparing with the result of the existing example.\",\"PeriodicalId\":425920,\"journal\":{\"name\":\"2017 International Conference on Grey Systems and Intelligent Services (GSIS)\",\"volume\":\"105 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Grey Systems and Intelligent Services (GSIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSIS.2017.8077704\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Grey Systems and Intelligent Services (GSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2017.8077704","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grey decision model based on three-parameter interval grey number
For the multi-index decision problem with uncertain information, this paper introduces the definition of interval distance of three-parameter interval grey number, proposes the relative degree of grey incidence based on interval distance of three-parameter interval grey number, constructs the grey incidence decision-making model with three-parameter interval grey number, measures the relative degree of grey incidence based on interval distance, and sorts the decision schemes according to the relative degree of grey incidence to select the best scheme. Finally, this model is applied to a practical decision-making problem, and the feasibility of the method is proved by comparing with the result of the existing example.