An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information

Abstract

Large scale group decision making (LGDM) problems and social network analysis (SNA) methods are both attracting increasing attention, and SNA methods are useful for addressing LGDM problems. By considering social network information, a new interval type-2 fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model is proposed to solve LGDM problems in complex and uncertain environments. First, a SNA community detection method is applied to reduce the complexity of large scale decision makers (DMs) according to the social connections among them. Then, interval type-2 fuzzy sets (IT2 FSs) and linguistic variables are employed to handle the uncertainties, and the TOPSIS method is improved using IT2 FSs to obtain an optimal alternative for a LGDM problem. Next, decision weights are computed based on the centrality of the SNA, and the decision information is aggregated by the interval type-2 fuzzy weighted average method. The procedure for solving LGDM problems is presented. Finally, an illustrative example is investigated to demonstrate the feasibility of the proposed solution for LGDM problems, and the results are compared with those of an existing method to verify the validity of the new proposed method.