Consensus Progress based on Intuitionistic Fuzzy Sets for GDM with Incomplete Information

In existing approaches for consensus reaching processes (CRPs) of group decision making (GDM) with incomplete information, few estimation methods have addressed the experts’ hesitation information, leading to missing values. Most current consensus approaches calculate the consensus level by measuring the similarity degrees between the individual experts’ assessment and the collective one, which causes counterintuitive consensus results when extreme values are present in the assessments given by experts. This paper proposes a consensus framework in which the consensus level is obtained by means of the similarity degrees measured among individual experts’ assessments. First, to model the hesitation degree of experts and maintain the authenticity of expert assessments, intuitionistic fuzzy sets (IFSs) are developed to estimate the missing values in incomplete intuitionistic fuzzy decision matrices. Then, a consensus model is constructed based on the intuitionistic fuzzy similarity function. Finally, a numerical example is presented to demonstrate the application of the proposed method. By comparison analysis, the effectiveness and advantages of the proposed framework are shown.