Research on uncertain multiple attribute decision making based on improved possibility degree relation model and its application

Abstract

In this paper, we mainly focus on the triangular fuzzy number-based uncertain multiple attribute decision making problem, which not only attribute weights are unknown but also preference for decision-making objects do not exist, and give a new decision-making approach and consistency test method based on the improved possibility relation model. This method fully considers the influence of the comparison possibility degree measurement information between the measured values of the indicator attribute itself in the course of processing uncertain reasoning such as fuzziness and randomness, reconstructs a new and more optimized triangular fuzzy number-based decision making object improved possibility degree relation model (TFN-DMOIPDRM) to determine the attribute weight vector, and creatively designs a general formula to test the consistency of the improved possibility degree relation matrix. Next the criterion of consistency test is given to solve the remaining problem of the lack of consistency test in the construction of fuzzy number possibility degree relation model and fuzzy complementary judgment matrix. Moreover, another major contribution of this paper is to present the improved possibility degree relation theory of triangular fuzzy numbers on the basis of defining the comparative possibility degree of triangular fuzzy numbers, and fully study a series of its relevant properties and conclusions, as well as its application in constructing model to determine the attribute weight. By aggregating the improved possibility degree information measured by pairwise comparing decision objects, the overall improved possibility degree value of each decision object is obtained, and a new algorithm of the improved possibility relation model for triangular fuzzy number multi-attribute decision object is given, which improves and expands the application of fuzzy number possibility theory in the field of fuzzy uncertainty reasoning and intelligent computing of artificial intelligence.