Taxonomy-based multiple attribute group decision making method with probabilistic uncertain linguistic information and its application in supplier selection

Abstract

The optimal supplier selection in medical instrument industries could be considered a classical MAGDM issue. The probabilistic uncertain linguistic term sets (PULTSs) could depict uncertain information well and the Taxonomy method is appropriate to compare various alternatives according to their merits and utility degree from studied attributes. In such paper, we develop a Taxonomy method for probabilistic uncertain linguistic MAGDM (PUL-MAGDM) with the completely unknown attribute weights. Above all, the score function’s definition is utilized to derive the weights of attribute based upon the CRITIC method. In addition, the probabilistic uncertain linguistic development pattern (PULDP) is improved and the smallest development attribute value from the positive ideal solution under PULTSs is calculated to determine the optimal alternative. In the end, taking the supplier selection in medical instrument industries as an example, we demonstrate the usage of the developed algorithms. Based on this, the comparison of methods is conducted with existing methods, such as PUL-TOPSIS method, the PULWA operator, the PUL-EDAS method and the ULWA operator. The results verify that the decision-making framework is valid and effective for supplier selection. Thus, the advantage of this designed method is that it is simple to understand and easy to compute. The designed method can also contribute to the selection of suitable alternative successfully in other selection issues.