Consensus checking and improving methods for AHP with q-rung dual hesitant fuzzy preference relations. (1st December 2022)

• Record Type: Journal Article
• Authors: Xu, Yuan; Liu, Shifeng; Wang, Jun; Shang, Xiaopu
• Is Part Of: Expert systems with applications. Volume 208(2022)
• Publication Date: 2022-12-01
• Subjects: Q-rung dual hesitant fuzzy AHP -- Q-rung dual hesitant preference relations -- Consensus reaching process -- MCGDM; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117902
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose a novel AHP method with q-rung dual hesitant fuzzy preference relations. Methods to check and improve the consistency of individual matrix are introduced. Methods to check and improve the consensus of multiple matrices are introduced. A novel calculation method of priority weights is put forth. A complete decision support model for MCGDM problems with q-RDHPRs is proposed. Abstract: Decision makers (DMs) are often hesitant about the evaluations for subjective or objective reasons, and established preference relations always have various limitations on the way that DMs express themselves. Aiming at investigating the consensus reaching process where DMs needs more decision freedom, this study proposes a novel decision support model for AHP with q-rung dual hesitant fuzzy preference relations (q-RDHFPRs). To do this, we give the definition of q-RDHFPRs and explore the corresponding operational rules. On account of this, we propose a family of algorithms to check and improve consistency and consensus of q-RDHFPRs, which can automatically obtain scientific and effective consensus results. Moreover, a priority method for q-RDHFPRs is proposed to rank the alternatives. The procedure of the q-RDHF-AHP is given in detail, and the example of risk evaluation of Hospital-acquired infections is employed to demonstrate our results. Comparative analyses show that the proposed q-RDHF-AHP method is more powerful for coping with the hesitant and uncertain situations and greatly expands the information description scope of the method.