An interactive method for dynamic intuitionistic fuzzy multi-attribute group decision making

Authors:
Zhi-Xin Su;Ming-Yuan Chen;Guo-Ping Xia;Li Wang
Affiliations:
School of Economics and Management, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, PR China and Department of Mechanical and Industrial Engineering, Concordia Universit ...;Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve W., Montreal, Quebec, Canada H3G 1M8;School of Economics and Management, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, PR China;School of Economics and Management, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This paper investigates the dynamic intuitionistic fuzzy multi-attribute group decision making (DIF-MAGDM) problems, in which all the attribute values provided by multiple decision makers (DMs) at different periods take the form of intuitionistic fuzzy numbers (IFNs), and develops an interactive method to solve the DIF-MAGDM problems. The developed method first aggregates the individual intuitionistic fuzzy decision matrices at different periods into an individual collective intuitionistic fuzzy decision matrix for each decision maker by using the dynamic intuitionistic fuzzy weighted averaging (DIFWA) operator, and then employs intuitionistic fuzzy TOPSIS method to calculate the individual relative closeness coefficient of each alternative for each decision maker and obtain the individual ranking of alternatives. After doing so, the method utilizes the hybrid weighted averaging (HWA) operator to aggregate all the individual relative closeness coefficients into the collective relative closeness coefficient of each alternative and obtain the aggregate ranking of alternatives, by which the optimal alternative can be selected. In addition, the spearman correlation coefficient for both the aggregate ranking and individual ranking of alternatives is calculated to measure the consensus level of the group preferences. Finally, a numerical example is used to illustrate the developed method.