A novel ranking method for intuitionistic fuzzy set based on information fusion and application to threat assessment

Abstract

A novel ranking method based on multi-time information fusion is proposed for intuitionistic fuzzy sets (IFSs) and applied to the threat assessment problem, a multi-attribute decision making (MADM) one. This method integrates a designed intuitionistic fuzzy entropy (IFE), the closeness degree of technique for order preference by similarity to ideal solution (TOPSIS), the decision maker¡¯s (DM¡¯s) risk attitude and decision information from DMs in multiple times. Firstly, a novel IFE is designed in order to sufficiently express the fuzziness and the unknown of uncertainty conveyed by IFSs. Secondly, considering the distance from the positive ideal solution and the negative ideal solution simultaneously, the closeness degree of TOPSIS is utilized to characterize the amount of information conveyed by the IFS. The amount of information and the uncertainty of information are combined by the introduction of DMs¡¯ risk attitudes and then; a novel single time ranking method for IFSs is structured. Next, to reflect the subjective ranking intention on alternatives, the comprehensive preference model of DMs is established. The optimal attribute weights are derived from the linear programming model which incorporates the subjective ranking intension and the objective ranking result. Finally, the decision information from DMs in multiple times is aggregated to give the final ranking results. A threat assessment example and comparison analysis demonstrates the flexibility and effectiveness of the proposed method.