Managing consensus and weights in iterative multiple-attribute group decision making

被引：51

作者：

Dong, Yucheng ^{[1
]}

Xiao, Jing ^{[1
]}

Zhang, Hengjie ^{[1
]}

Wang, Tao ^{[1
]}

机构：

[1] Sichuan Univ, Sch Business, Chengdu 610065, Peoples R China

来源：

APPLIED SOFT COMPUTING | 2016年 / 48卷

关键词：

Multiple attribute group decision making; Consensus; Minimum adjustments; Preferences-modifying; Weights-updating; FUZZY PREFERENCE RELATIONS; LINGUISTIC ASSESSMENTS; SUPPORT-SYSTEM; AGGREGATION OPERATORS; REACHING PROCESS; MODEL; INFORMATION; METHODOLOGY; RULES;

D O I：

10.1016/j.asoc.2016.06.029

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study put forwards a novel consensus framework to manage the consensus and weights (i.e., weights of the experts and attributes) in iterative multiple-attribute group decision making (MAGDM) problem. In this consensus framework, an optimization-based consensus model is devised to support the process of preferences-modifying, which seeks to minimize the adjustment amounts (in the sense of Manhattan distance) between the original and adjusted preferences. Then, the other two optimization-based consensus models are constructed to support the weights-updating, in which the consensus level among experts can be further improved. A numerical example is provided to show the application of the proposed consensus framework, and a detailed comparison analysis is presented to verify the effectiveness of the proposed consensus framework. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：80 / 90

页数：11

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