MADM method based on prospect theory and evidential reasoning approach with unknown attribute weights under intuitionistic fuzzy environment. (1st December 2017)

• Record Type: Journal Article
• Authors: Bao, Tiantian; Xie, Xinlian; Long, Peiyin; Wei, Zhaokun
• Is Part Of: Expert systems with applications. Volume 88(2017)
• Page Start: 305
• Page End: 317
• Publication Date: 2017-12-01
• Subjects: Decision analysis -- Multi-attribute decision making -- Prospect theory -- Evidential reasoning approach -- Entropy; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.07.012
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The extended measures of intuitionistic fuzzy entropy and cross entropy are proposed. The proposed optimization models ensure the objectivity and reasonability of weights. The proposed approach can reduce the loss of decision making information. The decision results based on human bounded rationality can be more realistic. Abstract: This paper proposes an intuitionistic fuzzy decision method based on prospect theory and the evidential reasoning approach, aiming at analyzing multi-attribute decision making problems in which the criteria values are intuitionistic fuzzy numbers and the information of attributes weights is unknown. Firstly, the measures of entropy and cross entropy are defined for intuitionistic fuzzy sets by taking into consideration the preference of decision maker towards hesitancy degree. Secondly, combined with bounded rationality, the prospect decision matrix is calculated in the light of prospect theory and intuitionistic fuzzy distance. Thirdly, the correlational analyses are conducted between the attribute weights and three indicators which are entropy, cross entropy and prospect value, and optimization models for identifying attribute weights are built under the circumstances that the weights are incomplete and unknown. Finally, in order to avoid the loss of decision making information, the evidential reasoning approach is applied to the calculation of comprehensive prospective values for all alternatives. Following the value calculation, the ranking and the optimal alternative are determined based on the comprehensive prospective values. Illustrating examples demonstrate that the proposed method is reasonable and feasible.