A new fuzzy dempster MCDM method and its application in supplier selection

Supplier selection is a multi-criterion decision making problem under uncertain environments. Hence, it is reasonable to hand the problem in fuzzy sets theory (FST) and Dempster Shafer theory of evidence (DST). In this paper, a new MCDM methodology, using FST and DST, based on the main idea of the technique for order preference by similarity to an ideal solution (TOPSIS), is developed to deal with supplier selection problem. The basic probability assignments (BPA) can be determined by the distance to the ideal solution and the distance to the negative ideal solution. Dempster combination rule is used to combine all the criterion data to get the final scores of the alternatives in the systems. The final decision results can be drawn through the pignistic probability transformation. In traditional fuzzy TOPSIS method, the quantitative performance of criterion, such as crisp numbers, should be transformed into fuzzy numbers. The proposed method is more flexible due to the reason that the BPA can be determined without the transformation step in traditional fuzzy TOPSIS method. The performance of criterion can be represented as crisp number or fuzzy number according to the real situation in our proposed method. The numerical example about supplier selection is used to illustrate the efficiency of the proposed method.

Research highlights
► A new Multi-Criterion Decision Making (MCDM) methodology, using fuzzy sets theory (FST) and Dempster Shafer Theory of evidence (DST), is developed to deal with supplier selection problem.
► The Basic Probability Assignments (BPA) can be determined by the distance to the ideal solution and the distance to the negative ideal solution. The proposed method is more flexible due to the reason that the BPA can be determined without the transformation step in traditional fuzzy TOPSIS method.
► The numerical example about supplier selection is used to illustrate the efficiency of the proposed method.