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Granular Computing
Erschienen 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

verfasst von: Zhiming Zhang

Erschienen in: Granular Computing | Ausgabe 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.
Vorheriger Artikel Neighborhood attribute reduction approach to partially labeled data
Nächster Artikel Improved composite relation for pythagorean fuzzy sets and its application to medical diagnosis

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Metadaten
Titel
Maclaurin symmetric means of dual hesitant fuzzy information and their use in multi-criteria decision making
verfasst von
Zhiming Zhang
Publikationsdatum
14.01.2019
Verlag
Springer International Publishing
Erschienen in
Granular Computing / Ausgabe 2/2020
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-018-00152-4

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