Social network analysis-based consensus-supporting framework for large-scale group decision-making with incomplete interval type-2 fuzzy information

Abstract Experts are assumed to be mutually independent and incomplete decision matrices are used, in traditional large-scale group decision-making (LSGDM). Social network analysis (SNA) methods are useful for managing LSGDM problems considering the social relationships among experts. This study develops a novel SNA-based decision framework for addressing LSGDM problems with incomplete interval type-2 fuzzy information. First, a community detection algorithm is designed to classify experts, and a trust propagation and aggregation mechanism is developed to obtained experts’ indirect trust values and weights based on the social trust network. Second, a multi-objective programming model integrated with trust relationships among experts is constructed to estimate the missing values in incomplete interval type-2 fuzzy decision matrices. The proposed estimation approach is robust and effectively estimates missing values while maintaining high discrimination levels. Subsequently, an SNA-based consensus-reaching model is designed for achieving a group consensus based on a twofold feedback adjustment mechanism, namely, adaptive judgment and weight feedback. Finally, an illustrative example is presented to demonstrate the applicability of the proposed approach. A simulation and comparison study is conducted to verify the efficiency and advantages of the SNA-based consensus-supporting framework for LSGDM.