Intuitionistic fuzzy TOPSIS method based on CVPIFRS models: An application to biomedical problems

In order to obtain the weights of a set of criteria by means of real-world data, an effective method based on the covering-based variable precision intuitionistic fuzzy rough set (CVPIFRS) models is presented. By combining the CVPIFRS models with the idea of TOPSIS, we propose a decision-making method to effectively settle the complex and changeable bone transplant selections, which is one of typical multi-attribute decision-making (MADM) problems. The sensitivity analysis of the proposed method shows that the approach is highly flexible and can be applied to a wide range of environments by adjusting the values of the intuitionistic fuzzy (IF) variable precision, together with the choice of different IF logical operators. Through a comparison of the proposed method and some existing MADM methods, it is shown that our method is more effective in dealing with these complex and changeable bone transplant selections issues.