Maclaurin symmetric mean aggregation operators based on t-norm operations for the dual hesitant fuzzy soft set
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The objective of this paper is to present a Maclaurin symmetric mean (MSM) operator to aggregate dual hesitant fuzzy (DHF) soft numbers. The salient feature of MSM operators is that it can reflect the interrelationship between the multi-input arguments. Under DHF soft set environment, we develop some aggregation operators named as DHF soft MSM averaging (DHFSMSMA) operator, the weighted DHF soft MSM averaging (WDHFSMSMA) operator, DHF soft MSM geometric (DHFSMSMG) operator, and the weighted DHF soft MSM geometric (WDHFSMSMG) operator. Further, some properties and the special cases of these operators are discussed. Then, by utilizing these operators, we develop an approach for solving the multicriteria decision-making problem and illustrate it with a numerical example. Finally, a comparison analysis has been done to analyze the advantages of the proposed operators.
KeywordsMaclaurin symmetric mean Aggregation operator Multicriteria decision-making Dual hesitant fuzzy soft set
The authors are thankful to the editor and anonymous reviewers for their constructive comments and suggestions that helped us in improving the paper significantly. The authors (Rishu Arora) would like to thank the Department of Science & Technology, New Delhi, India for providing financial support under WOS-A scheme wide File No. SR/WOS-A/PM-77/2016 during the preparation of this manuscript.
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Conflict of interest
The authors declare that they have no conflict of interest.
Human/animal rights statement
This article does not contain any studies with human participants or animals performed by any of the authors.
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