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

• Published in: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
• Main Authors: Ma, Xiuqin; Sun, Huanling; Qin, Hongwu; Wang, Yibo; Zheng, Yan
• Format: Article
• Language: English
• Published: IOS PRESS 2024
• Subjects: Computer Science
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001193319500080

When handling complex uncertainty information for multi-attribute decision-making (MADM) problems, intervalvalued 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 ofMADM. 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.