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Information 2018, 9(8), 201; https://doi.org/10.3390/info9080201

Dual Generalized Nonnegative Normal Neutrosophic Bonferroni Mean Operators and Their Application in Multiple Attribute Decision Making

School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, Fujian, China
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Received: 19 July 2018 / Revised: 27 July 2018 / Accepted: 27 July 2018 / Published: 6 August 2018
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Abstract

For multiple attribute decision making, ranking and information aggregation problems are increasingly receiving attention. In a normal neutrosophic number, the ranking method does not satisfy the ranking principle. Moreover, the proposed operators do not take into account the correlation between any aggregation arguments. In order to overcome the deficiencies of the existing ranking method, based on the nonnegative normal neutrosophic number, this paper redefines the score function, the accuracy function, and partial operational laws. Considering the correlation between any aggregation arguments, the dual generalized nonnegative normal neutrosophic weighted Bonferroni mean operator and dual generalized nonnegative normal neutrosophic weighted geometric Bonferroni mean operator were investigated, and their properties are presented. Here, these two operators are applied to deal with a multiple attribute decision making problem. Example results show that the proposed method is effective and superior. View Full-Text
Keywords: multiple attribute decision making; nonnegative normal neutrosophic number; aggregation operator multiple attribute decision making; nonnegative normal neutrosophic number; aggregation operator
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mo, J.; Huang, H.-L. Dual Generalized Nonnegative Normal Neutrosophic Bonferroni Mean Operators and Their Application in Multiple Attribute Decision Making. Information 2018, 9, 201.

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