Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations

Abstract

The use of hesitant information in pairwise comparisons enriches the flexibility of qualitative decision making and allows for hesitant fuzzy linguistic preference relation (HFLPR). This paper develops separate consistency and consensus processes to deal with HFLPR individual rationality and group rationality. First, a possibility distribution approach and a 2-tuple linguistic model are introduced as support tools. Then, a new consistency measure is defined and a convergent algorithm described to aid the consistency improvement process in a given HFLPR. The algorithm adopts a local revision strategy and can be easily interpreted. Further, a direct consensus reaching process is presented to solve the HFLPR consensus problems. A prominent characteristic of this consensus reaching process is that the feedback system is based directly on the consensus degrees, thereby reducing the proximity measure calculations. Finally, the proposed consistency and consensus processes are applied to an investment project selection problem. The results and an in-depth comparative analysis verify the potential use and effectiveness of the proposed methods.