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      Some Generalized Pythagorean Fuzzy Bonferroni Mean Aggregation Operators with Their Application to Multiattribute Group Decision-Making

      , , , ,
      Complexity
      Hindawi Limited

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          Abstract

          The Pythagorean fuzzy set as an extension of the intuitionistic fuzzy set characterized by membership and nonmembership degrees has been introduced recently. Accordingly, the square sum of the membership and nonmembership degrees is a maximum of one. The Pythagorean fuzzy set has been previously applied to multiattribute group decision-making. This study develops a few aggregation operators for fusing the Pythagorean fuzzy information, and a novel approach to decision-making is introduced based on the proposed operators. First, we extend the generalized Bonferroni mean to the Pythagorean fuzzy environment and introduce the generalized Pythagorean fuzzy Bonferroni mean and the generalized Pythagorean fuzzy Bonferroni geometric mean. Second, a new generalization of the Bonferroni mean, namely, the dual generalized Bonferroni mean, is proposed by considering the shortcomings of the generalized Bonferroni mean. Furthermore, we investigate the dual generalized Bonferroni mean in the Pythagorean fuzzy sets and introduce the dual generalized Pythagorean fuzzy Bonferroni mean and dual generalized Pythagorean fuzzy Bonferroni geometric mean. Third, a novel approach to multiattribute group decision-making based on proposed operators is proposed. Lastly, a numerical instance is provided to illustrate the validity of the new approach.

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          Most cited references33

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          Pythagorean Membership Grades in Multicriteria Decision Making

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            Intuitionistic Fuzzy Aggregation Operators

            Zeshui Xu (2007)
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              Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets

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                Author and article information

                Journal
                Complexity
                Complexity
                Hindawi Limited
                1076-2787
                1099-0526
                2017
                2017
                : 2017
                :
                : 1-16
                Article
                10.1155/2017/5937376
                ded32862-a94e-4898-8969-12c61757504a
                © 2017

                http://creativecommons.org/licenses/by/4.0/

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