Supplier Selection Model Based on D Numbers and Transformation Function

Selecting reasonable suppliers can effectively improve the efficiency of enterprise supply chain management. Among them, expert evaluation is an important part of supplier selection problem, but the uncertainty, fuzziness and incompleteness of expert opinions make supplier selection problem difficult to solve. In order to systematically and effectively solve the uncertainty, ambiguity and incompleteness in supplier selection problem, this paper presents a new supplier selection method based on D numbers and transformation function. First, fuzzy preference relation is generated based on the decision matrix of pairwise comparisons given by experts. D numbers which can effectively deal with uncertain information extend fuzzy preference relation (D matrix). Second, the D matrix is converted into a crisp matrix form based on the integration representation of D numbers according to different situations whether or not the information in D matrix is complete. Third, the crisp matrix is converted into judgement matrix by using the transformation functions. Finally, analytic hierarchy process (AHP) method is applied based on the judgment matrix to give a priority weights for decision making. Three numerical examples and application of the supplier selection are used to show the feasibility and effectiveness of the proposed method

An improved method to determine basic probability assignment based on fuzzy mathematics

In D-S evidence theory, the determination of the basic probability assignment function (BPA) is the first and important step. However, the generation of BPA is still a problem to be solved. Based on the concepts in fuzzy mathematics, this paper proposes an improved BPA generation method. By introducing the value of the intersection point of membership function of different targets under the same index to describe the overlap degree of targets, the assignment of unknown items is optimized on this basis. This article applies it to target recognition of robot hands. The results show that the proposed method is more reliable and more accurate