Triangular Fuzzy Partitioned Bonferroni Mean Operators and Their Application to Multiple Attribute Decision Making

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Conference Proceeding
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 941-949
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© 2018 IEEE. The Bonferroni mean (BM) operator, introduced by Bonferroni, is a powerful tool to capture the interrelationship among aggregated arguments. Various generalizations and extensions of BM have developed and applied to solve many realworld problems. Recently, the notion of Partitioned Bonferroni mean (PBM) operator has been proposed with the assumption that the interrelationships do not always exist among all of the attributes. This work studies the PBM operator under triangular fuzzy environment. First, we propose a new fuzzy aggregation operator called the triangular fuzzy partitioned Bonferroni mean} (TFPBM) operator for aggregating triangular fuzzy numbers. Some properties and special cases of the new aggregation operator are also investigated. For the situations where the input arguments have different importance, we then define the triangular fuzzy weighted partitioned Bonferroni mean} (TFWPBM) operator. Furthermore, based on TFWPBM operator, an approach to deal with multiple attribute decision-making problems under triangular fuzzy environment is developed. Finally, a practical example is provided to illustrate the developed approach.
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