4.6 Article

Bonferroni mean operators of generalized trapezoidal hesitant fuzzy numbers and their application to decision-making problems

期刊

SOFT COMPUTING
卷 25, 期 6, 页码 4925-4949

出版社

SPRINGER
DOI: 10.1007/s00500-020-05504-4

关键词

Hesitant fuzzy set; Hesitant fuzzy number; Generalized trapezoidal hesitant fuzzy numbers; Bonferroni mean; Geometric Bonferroni mean; Arithmetic Bonferroni mean; Multiple attribute decision making

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This work investigates multiple attribute decision-making problems with generalized trapezoidal hesitant fuzzy numbers, developing aggregation techniques and decision-making methods for analyzing and discussing decision processes under generalized trapezoidal hesitant fuzzy environments. The proposed approaches based on Bonferroni aggregation operators are applied to multicriteria decision making, with practical examples provided to illustrate the results, followed by a comparative analysis with existing methods.
Generalized trapezoidal hesitant fuzzy numbers are useful when ever there is indecision among several possible values for the preferences over objects in the process of decision making. In this sense, the aim of this work is to investigate the multiple attribute decision-making problems with generalized trapezoidal hesitant fuzzy numbers (GTHF-numbers). Therefore, we develop two aggregation techniques called generalized trapezoidal hesitant fuzzy Bonferroni arithmetic mean operator and generalized trapezoidal hesitant fuzzy Bonferroni geometric mean operator for aggregating the generalized trapezoidal hesitant fuzzy information. Then, we examine its properties and discuss its special cases. Also, we develop two approach for multiple attribute decision making under the generalized trapezoidal hesitant fuzzy environments. Also, we apply the proposed approaches based on Bonferroni aggregation operators under generalized trapezoidal hesitant fuzzy environments to multicriteria decision making, and we give two practical example to illustrate our results. In the end, we give an analysis of the proposed approaches by providing a brief comparative analysis of these methods with existing methods.

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