An extended MABAC method for multi-criteria group decision making based on intuitionistic fuzzy rough numbers. (1st August 2019)

• Record Type: Journal Article
• Authors: Jia, Fan; Liu, Yuanyuan; Wang, Xingyuan
• Is Part Of: Expert systems with applications. Volume 127(2019)
• Page Start: 241
• Page End: 255
• Publication Date: 2019-08-01
• Subjects: Atanassov intuitionistic fuzzy sets -- Rough numbers -- MABAC -- MCGDM; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.03.016
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel concept of intuitionistic fuzzy rough numbers (IFRN) is proposed. Arithmetic operations and aggregating operators of IFRN are presented. An IFN-based MABAC model and an IFRN-based MABAC model are proposed respectively. Abstract: Considering multi-criteria group decision-making (MCGDM) problems under uncertain environment, this paper presents a novel concept of intuitionistic fuzzy rough numbers (IFRNs), which is fused by intuitionistic fuzzy numbers (IFNs) and rough numbers (RNs), and proposes a new approach for MCGDM based on IFRNs. The proposed approach can aggregate group information and obtain the final decision results in an objective and effective way. First, we propose the constructing process of IFRNs and discuss the arithmetic operations, ranking rules, aggregation operators, as well as some corresponding properties. Then the novel concept of IFRN is used to aggregate intuitionistic fuzzy information given by decision group. Based on the aggregated IFRNs, the MABAC model is extended from two perspectives, obtaining an IFN-based MABAC model and an IFRN-based MABAC model respectively. Finally an empirical example of supplier selection for medical devices is utilized to show the application of proposed models, and comparisons with five traditional models, of which three refer to information aggregating and two refer to alternatives selecting, are made to validate the effectiveness and superiority of proposed methods.