Intuitionistic fuzzy TOPSIS multi-attribute decision making method based on revised scoring function and entropy weight method

In multi-attribute decision-making problems, vague decision information is well-represented by intuitionistic fuzzy sets. However, many of the scoring functions of existing methods cannot always obtain a ranking for the alternatives. In this paper, a TOPSIS -based decision-making method is proposed for multi-attribute decision-making problems in which the attribute weights are unknown and the decision information is in the form of intuitionistic fuzzy numbers. First, a revised definition of the scoring function is introduced and used to solve the intuitionistic fuzzy entropy, which is then used to objectively determine the attribute weights. Second, intuitionistic fuzzy-weighted geometric operators are used to integrate the information. The positive and negative ideal solutions of the comprehensive attribute values are determined, and the similarities between each alternative and the positive and negative ideal solutions are calculated. Finally, the alternatives set is ranked by comparing the relative closeness of the alternatives. This proposed method increases the range of applications of the traditional entropy-weighted method. Moreover, it does not require the decision-maker to specify the attribute weights in advance. The results hence tend to be more objective. Examples comparing this method with existing TOPSIS-based methods illustrate its practicality.