A novel multi-attribute decision making method based on interval-valued fermatean fuzzy bonferroni mean operators

• Published in: Journal of Intelligent and Fuzzy Systems
• Main Author: Ma X.; Sun H.; Qin H.; Wang Y.; Zheng Y.
• Format: Article
• Language: English
• Published: IOS Press BV 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185540062&doi=10.3233%2fJIFS-235495&partnerID=40&md5=9b293a78f8a73be25f30762740614989

When handling complex uncertainty information for multi-attribute decision-making (MADM) problems, interval-valued Fermatean fuzzy sets (IVFFSs) are a novel and powerful tool with a wide range of prospective applications. However, existing MADM methods based on IVFFS ignore associations between attributes and are vulnerable to extreme values. Thus, this research proposes a novel MADM method based on IVFFSs. First, taking into consideration attribute relationships, we propose interval-valued Fermatean fuzzy Bonferroni mean (IVFFBM) operators and interval-valued Fermatean fuzzy weighted Bonferroni mean (IVFFWBM) operators based on IVFFSs. Further, interval-valued Fermatean fuzzy power Bonferroni mean (IVFFPBM) operator and interval-valued Fermatean fuzzy weighted power Bonferroni mean (IVFFWPBM) operator are suggested considering the impact of extreme values. Secondly, Attribute weights are a key component of MADM. A novel method for determining attribute weights based on fuzzy entropy is developed. Finally, a novel MADM approach is proposed based on the proposed operator and weight determination method. Experimental results on one real-life case demonstrate the superiority and effectiveness of our method. © 2024 – IOS Press. All rights reserved.