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

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