scholarly journals 2-Dimension Uncertain Linguistic Generalized Normalized Weighted Geometric Bonferroni Mean and Its Application in Multiple Attribute Decision Making

2017 ◽  
Vol 0 (0) ◽  
pp. 0-0 ◽  
Author(s):  
Peide Liu
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Bonferroni Mean ◽  
Multiple Attribute ◽  
Geometric Bonferroni Mean

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

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 201 ◽  
Author(s):  
Jiongmei Mo ◽  
Han-Liang Huang
Keyword(s):  
Decision Making ◽  
Score Function ◽  
Information Aggregation ◽  
Multiple Attribute Decision Making ◽  
Ranking Method ◽  
Bonferroni Mean ◽  
Operational Laws ◽  
Multiple Attribute ◽  
Geometric Bonferroni Mean ◽  
Neutrosophic Number

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.

scholarly journals Pythagorean Fuzzy Interaction Partitioned Bonferroni Mean Operators and Their Application in Multiple-Attribute Decision-Making

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-25 ◽  
Author(s):  
Wei Yang ◽  
Jiarong Shi ◽  
Yong Liu ◽  
Yongfeng Pang ◽  
Ruiyue Lin
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Investment Company ◽  
Pythagorean Fuzzy Set ◽  
Bonferroni Mean ◽  
Multiple Attribute ◽  
Special Cases ◽  
Geometric Bonferroni Mean

The aim of this paper is to develop partitioned Pythagorean fuzzy interaction Bonferroni mean operators based on the Pythagorean fuzzy set, Bonferroni mean, and interaction between membership and nonmembership. Several new aggregation operators are developed including the Pythagorean fuzzy interaction partitioned Bonferroni mean (PFIPBM) operator, the Pythagorean fuzzy weighted interaction partitioned Bonferroni mean (PFWIPBM) operator, the Pythagorean fuzzy interaction partitioned geometric Bonferroni mean (PFIPGBM) operator, and the Pythagorean fuzzy weighted interaction partitioned geometric Bonferroni mean (PFWIPGBM) operator. Some main properties and some special particular cases of the new operators are studied. Many existing operators are the special cases of new aggregation operators. Moreover, a multiple-attribute decision-making method based on the proposed operator has been developed and the investment company selection problem is presented to illustrate feasibility and practical advantages of the new method.

scholarly journals Hesitant Fuzzy Generalised Bonferroni Mean Operators Based on Archimedean Copula for Multiple-Attribute Decision-Making

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ju Wu ◽  
Fang Liu ◽  
Yuan Rong ◽  
Yi Liu ◽  
Chengxi Liu
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Information Fusion ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operator ◽  
Archimedean Copula ◽  
Fuzzy Information ◽  
Bonferroni Mean ◽  
Operation Rules ◽  
Multiple Attribute

Information fusion is an important part of multiple-attribute decision-making, and aggregation operator is an important tool of decision information fusion. Integration operators in a variety of fuzzy information environments have a slight lack of consideration for the correlation between variables. Archimedean copula provides information fusion patterns that rely on the intrinsic relevance of information. This paper extends the Archimedean copula to the aggregation of hesitant fuzzy information. Firstly, the Archimedean copula is used to generate the operation rules of the hesitant fuzzy elements. Secondly, the hesitant fuzzy copula Bonferroni mean operator and hesitant fuzzy weighted copula Bonferroni mean operator are propounded, and several properties are proved in detail. Furthermore, a decision-making method based on the operators is proposed, and the specific decision steps are given. Finally, an example is presented to illustrate the practical advantages of the method, and the sensitivity analysis of the decision results with the change of parameters is carried out.

An Approach to Multiple Attribute Decision Making with Triangular Linguistic Information

2016 ◽  
Vol 13 (10) ◽  
pp. 7289-7292
Author(s):  
Rong-Fang Chen
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Project Risk ◽  
Linguistic Information ◽  
Bonferroni Mean ◽  
Aggregation Techniques ◽  
Multiple Attribute ◽  
Special Cases ◽  
The Ideal

In this paper, we investigate the multiple attribute decision making problems with triangular linguistic information. Motivated by the ideal of Bonferroni mean, we develop the aggregation techniques called the triangular linguistic Bonferroni mean (TLBM) operator for aggregating the triangular linguistic information. We study its properties and discuss its special cases. For the situations where the input arguments have different importance, we then define the triangular linguistic weighted Bonferroni mean (TLWBM) operator, based on which we develop the procedure for multiple attribute decision making under the triangular linguistic environments. Finally, a practical example for evaluating the engineer project risk is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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