An attitudinal-based method for constructing intuitionistic fuzzy information in hybrid MADM under uncertainty

Abstract: Converting hybrid data in multiple attribute decision making (MADM) under uncertainty into intuitionistic fuzzy values (IFVs) is significant because the flexibility in handling vagueness or uncertainty of the latter can avoid the loss and distortion of the original decision information and thus guarantee the mildness of fuzzy MADM and the reliability of the final decision results. In this paper, we develop an attitudinal-based method for constructing intuitionistic fuzzy information according to the attribute values expressed in different data types in hybrid MADM. By introducing a basic unit-interval monotonic (BUM) function Q, we extract the attitudinal character from a person’s information about his/her decision attitude, and formalize the person’s subjective opinions against alternatives as IFVs based on the expected attribute values associated with attitude Q, thus transforming a hybrid decision matrix, with full consideration of a person’s attitude, into an intuitionistic fuzzy decision matrix. The intuitionistic fuzzy aggregation operators are then used to aggregate the intuitionistic fuzzy attribute values of each alternative and a new approach is employed to rank these intuitionistic fuzzy alternatives based on the amount of information and its reliability. Finally, an example is provided to illustrate the proposed approach and to examine its feasibility and validity. [Copyright &y& Elsevier]