A Granular Computing-Driving Hesitant Fuzzy Linguistic Method for Supporting Large-Scale Group Decision Making
针对大规模群体决策中决策者偏好信息模糊且难以达成一致的问题，提出一种基于粒计算的犹豫模糊语言方法，通过引入粒度层次和一致性指标，结合模糊C均值聚类，有效整合偏好信息并提高决策一致性。
Considering the conditions that: 1) same linguistic term means different things for different people; 2) flexible semantics cannot be represented by original linguistic term; and 3) some semantics given by decision makers are possible to be changed during the consistency improving process, we bring some flexibility and personality into hesitant fuzzy linguistic preference matrix structures by allowing the linguistic preference matrices to be granular rather than numeric, providing a new characterization of linguistic preference matrices. Inspired by the thought of granular computing, this article proposes a new hesitant fuzzy linguistic method to deal with issues when a lot of decision makers provide hesitant and uncertain preference information in the decision-making process. First, we design a multiplicative consistency index and calculate its thresholds corresponding to different dimensions of preference matrix by the Monte Carlo experiment. Then, we construct a hesitant fuzzy linguistic model with granularity level, so as to recharacterize original assessment information and improve the consistency of preference matrices as far as possible. Considering the features of some large-scale group decision-making situation, where the decision makers have little opportunity to take part in multiple consensus reaching processes, hesitant fuzzy linguistic fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> -means clustering algorithm is developed to integrate the assessment information given by decision makers. Finally, the final decision-making results are derived. An illustrative example of assessing psychological situation of some COVID-19 infected persons clarifies the reasonability of the proposed method. Finally, we complete some comparative studies and simulation experiments to demonstrate the method’s validity and advantages.