{"title":"Minimum Distance Method (MDM) for group judgment aggregations","authors":"Sida Zhou, D. Kocaoglu","doi":"10.1109/IEMC.1996.547917","DOIUrl":null,"url":null,"abstract":"A new aggregation method-Minimum Distance Method (MDMJ)-was developed to support the group decision process and to help decision makers achieve consensus under the framework of AHP. MDM employs the general distance concept, and is proven to be very appealing to the compromise nature of a group decision making. It preserves all characteristics of the functional equations approach proposed by Aczel and Saaty (1987). MDM is based on a goal programming model, which is easy to solve by using optimization software such as LINDO. Using the goal programming model, MDM provides the weighted membership capability. Sensitivity analysis can be performed to investigate the effect of varying the decision makers' relative importance in terms of weights in the goal programming model. Sensitivity analysis also allows us to make robust decisions.","PeriodicalId":138196,"journal":{"name":"IEMC 96 Proceedings. International Conference on Engineering and Technology Management. Managing Virtual Enterprises: A Convergence of Communications, Computing, and Energy Technologies","volume":"2000 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEMC 96 Proceedings. International Conference on Engineering and Technology Management. Managing Virtual Enterprises: A Convergence of Communications, Computing, and Energy Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMC.1996.547917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

A new aggregation method-Minimum Distance Method (MDMJ)-was developed to support the group decision process and to help decision makers achieve consensus under the framework of AHP. MDM employs the general distance concept, and is proven to be very appealing to the compromise nature of a group decision making. It preserves all characteristics of the functional equations approach proposed by Aczel and Saaty (1987). MDM is based on a goal programming model, which is easy to solve by using optimization software such as LINDO. Using the goal programming model, MDM provides the weighted membership capability. Sensitivity analysis can be performed to investigate the effect of varying the decision makers' relative importance in terms of weights in the goal programming model. Sensitivity analysis also allows us to make robust decisions.
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最小距离法(MDM)用于组判断聚合
在层次分析法的框架下,提出了一种新的集合方法——最小距离法(MDMJ)来支持群体决策过程,帮助决策者达成共识。MDM采用一般的距离概念,并且已被证明非常适合群体决策的折衷特性。它保留了Aczel和Saaty(1987)提出的泛函方程方法的所有特征。MDM基于目标规划模型,很容易通过使用LINDO等优化软件来解决这个问题。通过使用目标规划模型,MDM提供了加权成员能力。在目标规划模型中,可以通过敏感性分析来考察不同决策者的相对重要性在权重方面的影响。敏感性分析还允许我们做出可靠的决策。
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