基于直觉模糊相似度的逼近理想解排序方法及其应用

直觉模糊集在处理模糊性、犹豫模糊性等不确定性方面比传统模糊集更灵活和实用,是模糊集的进一步推广。相似度是刻画两个(直觉)模糊集相似程度的工具,在直觉模糊多属性决策中有重要应用,但通过对现有文献中所采用的相似度方法进行分析,发现在某些情形下反映的相似度与实际相似度不符,为此给出一种新的相似度计算公式,据此提出基于直觉模糊相似度的逼近理想点排序(TOPSIS)法,并将其应用到多属性决策问题中,最后通过实例说明其合理性和有效性。

The TOPSIS method based on intuitionistic fuzzy similarity and its application

Intuitionistic fuzzy sets are more flexible and practical than traditional fuzzy sets in dealing with vagueness, hesitant vagueness and other uncertainty aspects, they are further promotion of the fuzzy sets. The similarity ,a tool of describing similarity degree of two (intuitive) fuzzy sets, has an important application in intuitionistic fuzzy multi-attribute decision making. However, we find that in some cases the reflected similarity and the actual similarity are inconsistent by analyzing similarity method adopted by the existing literature, therefore, in this paper, a new kind of similarity calculation formula is presented, the TOPSIS method based on intuitionistic fuzzy similarity is proposed and it is applied to multiple attribute decision making problems, Finally, an example is given to illustrate the rationality and validity.