An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems

In order to deal with the fuzzy large-scale multiple-criteria group decision-making (FLMCGDM) problems, this paper incorporates clustering analysis and information aggregation operator into the problems of large-scale multiple-criteria group decision-making with interval type-2 fuzzy sets (IT2 FSs). The interval type-2 fuzzy equivalence clustering (IT2-FEC) analysis is used to classify decision-makers (DMs) to reduce the dimension of the large-scale DMs in the FLMCGDM problems. The combined weighted geometric averaging (CWGA) operator is extended into the case with IT2 FSs variables, which can take both the importance of individual and its relative position into account. Afterwards, a solution process for the FLMCGDM problems is proposed, in which the new equivalence clustering method and CWGA operator of IT2 FSs is incorporated. Finally, the reasonability and effectiveness of the proposed method are verified by an illustrative example. Compared with other methods, the IT2-FEC analysis can deal with the linguistic variables and produce dynamic clustering results in a more efficient way.