基于二元组语言信息的多属性群决策的灰色关联分析方法

G. Wei, Xiaorong Wang
{"title":"基于二元组语言信息的多属性群决策的灰色关联分析方法","authors":"G. Wei, Xiaorong Wang","doi":"10.1109/GSIS.2007.4443254","DOIUrl":null,"url":null,"abstract":"A new method is proposed to solve multiple attribute group decision making problems with linguistic assessment information. In the method, the two-tuple linguistic representation developed in recent years is used to aggregate the linguistic assessment information. According to the traditional ideas of grey relational analysis, the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and two-tuple linguistic positive ideal solution and two-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate.","PeriodicalId":445155,"journal":{"name":"2007 IEEE International Conference on Grey Systems and Intelligent Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Grey relational analysis method for multiple attribute group decision making based on two-tuple linguistic information\",\"authors\":\"G. Wei, Xiaorong Wang\",\"doi\":\"10.1109/GSIS.2007.4443254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new method is proposed to solve multiple attribute group decision making problems with linguistic assessment information. In the method, the two-tuple linguistic representation developed in recent years is used to aggregate the linguistic assessment information. According to the traditional ideas of grey relational analysis, the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and two-tuple linguistic positive ideal solution and two-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate.\",\"PeriodicalId\":445155,\"journal\":{\"name\":\"2007 IEEE International Conference on Grey Systems and Intelligent Services\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Grey Systems and Intelligent Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSIS.2007.4443254\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Grey Systems and Intelligent Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2007.4443254","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

提出了一种基于语言评价信息的多属性群决策方法。该方法采用近年来发展起来的二元语言表示对语言评价信息进行聚合。根据传统的灰色关联分析思想,通过计算每个备选方案与二元语言正理想解和二元语言负理想解的灰色关联度来确定最优备选方案。它基于这样的概念,即最优方案与正理想解的灰色关联度最大,与负理想解的灰色关联度最小。该方法在语言信息处理方面具有精确的特点。避免了以往在语言信息处理中出现的信息失真和丢失现象。最后,通过数值算例说明了该方法的应用。结果表明,该方法简单、有效、易于计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Grey relational analysis method for multiple attribute group decision making based on two-tuple linguistic information
A new method is proposed to solve multiple attribute group decision making problems with linguistic assessment information. In the method, the two-tuple linguistic representation developed in recent years is used to aggregate the linguistic assessment information. According to the traditional ideas of grey relational analysis, the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and two-tuple linguistic positive ideal solution and two-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信