Consistency and consensus driven for hesitant fuzzy linguistic decision making with pairwise comparisons

Abstract

Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because it provides an efficient way for opinion expression under uncertainty. For enhancing the theory of group decision making (GDM) with HFLPR, the paper introduces a method for addressing the GDM based on consistency and consensus measurements, which involves (1) defining a hesitant fuzzy linguistic geometric consistency index (HFLGCI) and proposing an algorithm for consistency checking and inconsistency improving for HFLPR; (2) proposing a worst consensus index based on the minimum similarity measure between each individual HFLPR and the overall perfect HFLPR in order to build a consensus reaching algorithm based on the acceptable HLFPRs. The convergence and monotonicity of the proposed two procedures is proved. Experiments are performed to investigate the critical values of the defined HFLGCI, and comparative analyses are conducted to show the effectiveness of the proposed method. A case concerning the performance evaluation of venture capital guiding funds is given to illustrate the applicability of the proposed method. As an application of our work, an online decision-making portal is finally provided for decision makers to utilize the proposed method to solve GDM with HFLPRs.