Fermatean fuzzy power Bonferroni aggregation operators and their applications to multi-attribute decision-making

• Published in: Soft computing (Berlin, Germany) Vol. 28; no. 1; pp. 191 - 203
• Main Authors: Ruan, Chuanyang, Chen, Xiangjing, Zeng, Shouzhen, Ali, Shahbaz, Almutairi, Bander
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2024
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Engineering; Fuzzy Systems and Their Mathematics; Mathematical Logic and Foundations; Mechatronics; Robotics; Bonferroni mean operator; Fermatean fuzzy set; Power average operator; Multi-attribute decision-making
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-09363-7

This paper focuses on the influence of support degree and weight between different attributes on the decision-making process. First, we analyze the Fermatean fuzzy power Bonferroni mean (FFPBM) and Fermatean fuzzy weighted power Bonferroni mean (FFWPBM) operators, which combine the properties of the Bonferroni mean and the power average operators. The proposal for a new operators can not only force decision-makers to consider the possible interaction between each attribute in the decision-making process, but also embrace the balance of data by calculating the support degree and aggregating the attribute values, thereby improving generalization ability overall. Then various qualities, such as idempotency, permutation, and boundedness, are demonstrated. After that, the MADM method is proposed with the developed operators. Finally, an example is provided to demonstrate the new approach's validity and viability.