Generalized hesitant fuzzy numbers and their application in solving MADM problems based on TOPSIS method

Generalized hesitant fuzzy numbers (GHFNs) are able to directly manage situations in which we may encounter a finite set of known values with a finite set of degrees of doubt as quantitative approximations of an uncertain situation/quantification of a linguistic expression. They are new extensions of hesitant fuzzy sets, which have been considered in this paper. In fact, in this paper, GHFNs will be utilized to model the uncertainty of the assessment values of options against criteria in multi-attribute decision making (MADM) problems. It means that all of the elements of decision matrix are GHFNs. Then, the technique for order of preference by similarity to ideal solution (TOPSIS) method, as a very successful method in solving MADM problems, will be updated to be used with GHFNs. To this end, the distance between GHFNs must be defined to obtain the distances between given alternatives from each of two subjective alternatives (positive/negative ideal solutions). Thus, three existing famous distance measures, i.e., general distance (d(g)), Hamming distance (d(h)), and Euclidean distance (d(e)) measures, have been updated for GHFNs firstly. Then, the new TOPSIS method will be proposed based on GHFNs. Finally, the numerical examples have been appointed to illustrate the proposed method, analyze comparatively and validate it.

Automated summary

Generalized hesitant fuzzy numbers (GHFNs) are used to handle situations where there is a finite set of known values and a finite set of degrees of doubt. This paper applies GHFNs to model the uncertainty in assessing options against criteria in multi-attribute decision making problems. The TOPSIS method is updated to work with GHFNs by defining distances between GHFNs and proposing a new TOPSIS method based on GHFNs.