Hesitant Fermatean fuzzy Bonferroni mean operators for multi-attribute decision-making

• Published in: Complex and Intelligent Systems
• Main Author: Wang Y.; Ma X.; Qin H.; Sun H.; Wei W.
• Format: Article
• Language: English
• Published: Springer International Publishing 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171171493&doi=10.1007%2fs40747-023-01203-3&partnerID=40&md5=e1555d2a20b2974e3e5c04b2e12105be

Hesitant Fermatean fuzzy sets (HFFS) can characterize the membership degree (MD) and non-membership degree (NMD) of hesitant fuzzy elements in a broader range, which offers superior fuzzy data processing capabilities for addressing complex uncertainty issues. In this research, first, we present the definition of the hesitant Fermatean fuzzy Bonferroni mean operator (HFFBM). Further, with the basic operations of HFFS in Einstein t-norms, the definition and derivation process of the hesitant Fermatean fuzzy Einstein Bonferroni mean operator (HFFEBM) are given. In addition, considering how weights affect decision-making outcomes, the hesitant Fermatean fuzzy weighted Bonferroni mean (HFFWBM) operator and the hesitant Fermatean fuzzy Einstein weighted Bonferroni mean operator (HFFEWBM) are developed. Then, the properties of the operators are discussed. Based on HFFWBM and HFFEWBM operator, a new multi-attribute decision-making (MADM) approach is provided. Finally, we apply the proposed decision-making approach to the case of a depression diagnostic evaluation for three depressed patients. The three patients' diagnosis results confirmed the proposed method's validity and rationality. Through a series of comparative experiments and analyses, the proposed MADM method is an efficient solution for decision-making issues in the hesitant Fermatean fuzzy environment. © The Author(s) 2023.