A novel multi-attribute decision-making method based on fuzzy rough sets

Abstract

The purpose of this paper is to propose a novel decision-making method based on fuzzy rough sets (FRSs) to deal with the uncertainty and imprecision existed in various multi-attribute decision-making (MADM) problems. In view of the effectiveness of fuzzy neighborhood operators in handling uncertain numerical data and the deficiencies of existing fuzzy neighborhood operators, we first define a reflexive fuzzy neighborhood operator in fuzzy information systems. Then, two types of FRS models are presented and their relationships are discussed. Whereafter, we use the tight FRS model to transform uncertain data into intuitionistic fuzzy data. A new MADM method is established under the intuitionistic fuzzy environment by using the idea of the PROMETHEE II and EDAS methods. Meanwhile, the intuitionistic fuzzy weights (IFWs) of attributes and the global intuitionistic fuzzy thresholds are introduced. Furthermore, a real-world example from the UCI database is utilized to expound the feasibility of the proposed method. At last, the validity and stability of the proposed method are demonstrated by comparative analysis and experimental analysis.