Accession number:  20110313600406

Title:  Approach to reaching consensus in multiple attribute group decision making

Authors:  Xu, Ying-Jun1 ; Li, Dong1  

Author affiliation:  1  Economics and Management College, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China


Corresponding author:  Xu, Y.-J. (xuyingjun5007@sina.com)  

Source title:  Kongzhi yu Juece/Control and Decision

Abbreviated source title:  Kongzhi yu Juece Control Decis

Volume:  25

Issue:  12

Issue date:  December 2010

Publication year:  2010

Pages:  1810-1814+1820

Language:  Chinese

ISSN:  10010920

CODEN:  KYJUEF

Document type:  Journal article (JA)

Publisher:  Northeast University, P.O. Box 125, Shenyang, 110005, China

Abstract:  In the process of group decision making, each expert provides the preferences over the alternatives with repect to each attribute, and constructs an individual decision matrix. As to the problem of consensus of the experts' decision matrix, an iterative method is proposed to complete consensus among group opinions, which can avoid forcing the experts to modify their opinions. Based on the consentaneous group decision matrix, the approach utilizes the multiplicative weighted aggregation operator to derive the overall attribute values of alternatives, by which the most desirable alternative can be found out. Then the implementation process of the approach is introduced detailedly with a practical example, which demonstrates the feasibility and practicability of the proposed method.

Number of references:  10

Main heading:  Iterative methods

Controlled terms:  Algorithms  -  Decision making  -  Mathematical operators

Uncontrolled terms:  Aggregation operator  -  Attribute values  -  Consensus  -  Decision matrices  -  Group decision  -  Group Decision Making  -  Implementation process  -  Iterative algorithm  -  Multiple attribute group

Classification code:  912.2 Management  -  921 Mathematics  -  921.6 Numerical Methods

Database:  Compendex

   Compilation and indexing terms, © 2011 Elsevier Inc.

非常感谢。