Exploring the effects of intuitionistic fuzzy separation measures on TOPSIS rankings

The purpose of this study is to extend the TOPSIS method for solving multiple attribute decision analysis problems with intuitionistic fuzzy data. Iintuitionistic fuzzy sets are capable of coping with imprecise information due to the fact that exact data may be difficult to be precisely determined since human judgments are often vague under many conditions. In this paper, a proposed intuitionistic fuzzy version of the TOPSIS method is presented and further deals with a comparative analysis of separation measures. For the sake of the comparison of intuitionistic fuzzy TOPSIS rankings yielded by different separation measures, a simulation experiment of different sizes was generated and examined. The consistency rate, the contradiction rate of the best alternative, and average Spearman correlation coefficients are utilized to conduct a pairwise comparison for all separation measures. The results which are inclusive of one hundred combinations of ten different categories of number for each alternatives and attributes indicate that the preference orders are hardly identical using different separation measures in the intuitionistic fuzzy TOPSIS method. The experimental analysis showed that the different definitions of IFS separations indeed significantly affect the final results by means of the intuitionistic fuzzy TOPSIS method. The comparative results presented in our experimental analysis indicate differentiations in a number of important aspects with some comparative indices.