An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation

In the process of multi-criteria decision making (MCDM), decision makers or experts usually exploit quantitative or qualitative methods to evaluate the comprehensive performance of all alternatives on each criterion. How the decision-makers or the experts make the evaluations relies on their professional knowledge and the actual performances on the criteria characters of the alternatives. However, because of both the objective complexity of decision making problem and the uncertainty of human subjective judgments, it is sometimes too hard to get the accurate evaluation information. Intuitionistic fuzzy set (IFS) is a useful tool to deal with uncertainty and fuzziness of complex problems. In this paper, we propose a new distance measure between IFSs and prove some of its useful properties. The experimental results show that the proposed distance measure between IFSs can overcome the drawbacks of some existing distance and similarity measures. Then based on the proposed distance measure, an extended intuitionistic fuzzy TOPSIS approach is developed to handle the MCDM problems. Finally, a practical application which is about credit risk evaluation of potential strategic partners is provided to demonstrate the extended intuitionistic fuzzy TOPSIS approach, and then it is compared with other current methods to further explain its effectiveness. (C) 2017 Elsevier Inc. All rights reserved.