共识条件下最小最大妥协的直觉模糊数集结方法研究

直觉模糊集（IFS）理论是描述广泛模糊事物的重要工具。为减小决策过程中个体偏好值与群体偏好值的差异，协调个体与群体间矛盾，提升决策结果的执行效率，本文提出最小化最大妥协度（Minimize maximum compromise）的决策准则。给出直觉模糊环境下的，个体偏好值的集结算法及决策方法，并分别考虑了有无共识条件、有无决策主体权重的三类决策情景。最后给出算例，验证本文方法的有效性，结果表明，相较于I-IFOWG算子，本文方法能够有效降低群体决策中个体的妥协程度。

Aggregation Method of Intuitionistic Fuzzy Numbers with Minimized Maximal Compromise under Consensus Condition

The intuitionistic fuzzy set (IFS) theory is an important tool for describing a wide range of fuzzy things and is widely used in group decision making theory. In this study, consensus decision making under intuitionistic fuzzy set is imvestigated, trying to minimize the maximum gap between individual preference and group preference. This investigation has practical meaning: A smaller gap means individuals are more satisfied with final decision, and the work efficiency will be higher when implement the decision. In this article, first, we define the concept of compromise degree and put forward three decision scenarios, which includes decision making without consensus constraints, with consensus constraints, and with weight. Second, a novel algorithm is developed to minimize the maximal compromise degree when aggregate individual decision information. At last, a numerical example is given to prove the validation of our algorithm and group decision method. The example shows, compared with Guiwu Wei's I-IFOWG operator, the compromise degree by our method is much lower. The study is expected to support intuitionistic fuzzy theory and consensus decision making theory.