A novel similarity measure based on new accuracy function on interval-valued intuitionistic fuzzy number and its application to multicriteria decision-making problem

Interval-valued intuitionistic fuzzy numbers (IVIFNs) are perfect to even more naturally model real-life issues and can be applied to several area like cluster analysis, decision-making, image processing, medical diagnosis, pattern recognition, etc. In particular, the similarity measures defined on IVIFNs take an important role in these fields effectively. Most of the researchers have tried to find a unique method (similarity measure) that may be appropriate for many problems. Apparently, there are several disadvantages to every one of them. First, in this paper, by using general accuracy function defined on IVIFNs, we define a new distance measure and hence a similarity measure for the class of IVIFNs. Second, some properties of these proposed measures are discussed by numerical problems. Third, the drawbacks of existing similarity measures are discussed and compared with the proposed similarity measure in various cases using numerical examples. Finally, the applicability of the proposed method for solving the problem of multicriteria decision making (MCDM) using TOPSIS technique is shown.