{"title":"基于犹豫信息的大规模共识模型","authors":"Rosa M. Rodríguez, L. Martínez, G. Tré","doi":"10.1142/9789813273238_0027","DOIUrl":null,"url":null,"abstract":"Nowadays due to the technological development, large-scale group decision making problems (LSGDM) are common and they often need to obtain accepted solutions for all experts involved in the problem. To do so, a consensus reaching process (CRP) is applied. A challenge in CRP for LSGDM is to overcome scalability problems. This paper presents a new consensus model to deal with LSGDM that is able to reduce the time cost of the CRP.","PeriodicalId":259425,"journal":{"name":"Data Science and Knowledge Engineering for Sensing Decision Support","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A consensus model for large scale using hesitant information\",\"authors\":\"Rosa M. Rodríguez, L. Martínez, G. Tré\",\"doi\":\"10.1142/9789813273238_0027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays due to the technological development, large-scale group decision making problems (LSGDM) are common and they often need to obtain accepted solutions for all experts involved in the problem. To do so, a consensus reaching process (CRP) is applied. A challenge in CRP for LSGDM is to overcome scalability problems. This paper presents a new consensus model to deal with LSGDM that is able to reduce the time cost of the CRP.\",\"PeriodicalId\":259425,\"journal\":{\"name\":\"Data Science and Knowledge Engineering for Sensing Decision Support\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science and Knowledge Engineering for Sensing Decision Support\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/9789813273238_0027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science and Knowledge Engineering for Sensing Decision Support","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9789813273238_0027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
随着技术的发展，大规模群体决策问题(large group decision making problem, LSGDM)越来越普遍，这些问题往往需要得到所有参与问题的专家都能接受的解决方案。为此，采用了共识达成过程(CRP)。针对LSGDM的CRP的一个挑战是克服可伸缩性问题。本文提出了一种新的共识模型来处理LSGDM，该模型能够降低CRP的时间成本。
A consensus model for large scale using hesitant information
Nowadays due to the technological development, large-scale group decision making problems (LSGDM) are common and they often need to obtain accepted solutions for all experts involved in the problem. To do so, a consensus reaching process (CRP) is applied. A challenge in CRP for LSGDM is to overcome scalability problems. This paper presents a new consensus model to deal with LSGDM that is able to reduce the time cost of the CRP.
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