An expertise-based consensus reaching process with probability-hesitant fuzzy preference relations and its application in risk assessment in food industry

Abstract

This paper proposes a novel expertise-based consensus reaching process (CRP) for probability-hesitant fuzzy preference relations (PHFPRs). First, to identify each expert's comprehensive expertise level, an expertise identification function is constructed based on consistency, hesitancy, and discrimination indicators of PHFPRs. Then, we introduce an expected value based expertise induced ordered weighted averaging (E-IOWA) operator for the aggregation of individual PHFPRs. Here, experts’ weights are objectively and dynamically assigned according to their identified expertise levels at each round. To prompt a consensus, this paper presents an expertise-based feedback mechanism so as to provide highly personalized direction rules for experts’ preference adjusting. Finally, a case study of risk assessment in food industry and comparative analysis are conducted to prove the validity of the proposed CRP.