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Granular Computing
Published in: Granular Computing 2/2020

14-01-2019 | Original Paper

Maclaurin symmetric means of dual hesitant fuzzy information and their use in multi-criteria decision making

Author: Zhiming Zhang

Published in: Granular Computing | Issue 2/2020

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Abstract

This paper investigates the Maclaurin symmetric mean (MSM) within the context of dual hesitant fuzzy sets and develops the dual hesitant fuzzy Maclaurin symmetric mean (DHFMSM), which can address the issues in previous dual hesitant fuzzy aggregation operators. Moreover, we put forward the geometric Maclaurin symmetric mean considering both the MSM and the geometric mean and apply it to propose a dual hesitant fuzzy geometric Maclaurin symmetric mean (DHFGMSM), followed by its several properties and special cases. Subsequently, considering the importance of each argument, the weighted DHFMSM and the weighted DHFGMSM are presented and used to develop an algorithm for realistic multi-criteria decision-making problems. Finally, the practicality of the new results is illustrated by a case study, and the advantages of the new results are highlighted by a comparison with other existing methods.
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Metadata
Title
Maclaurin symmetric means of dual hesitant fuzzy information and their use in multi-criteria decision making
Author
Zhiming Zhang
Publication date
14-01-2019
Publisher
Springer International Publishing
Published in
Granular Computing / Issue 2/2020
Print ISSN: 2364-4966
Electronic ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-018-00152-4

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