Narrow Results

In this study, a new version of TOPSIS method is reconstructed to deal with the problem of multi-criteria decision making. Here, the data representation of all alternatives is varied according to different criteria, such as real number, interval-valued number, set-valued number and intuitionistic fuzzy-valued number, etc. Because the distinguishing ability of each criterion can be reflected by its knowledge granularity, naturally, a knowledge granularity method is constructed to measure the criteria weights. Besides, the approach of how to select the ideal solution is redefined, especially for the case that the content of criterion according to all alternatives is not a totally ordered set anymore. What is more, the decision maker’s personal preference is considered, and the concrete indicator value can be calculated by the convex combination of the distance from possible alternatives to ideal solutions. Finally, the validity of the proposed decision-making algorithm is illustrated by a synthetic example.