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2022 | OriginalPaper | Buchkapitel

MADM Strategies Based on Arithmetic and Geometric Mean Operator Under Rough-Bipolar Neutrosophic Set Environment

verfasst von : Surapati Pramanik, Suman Das, Rakhal Das, Binod Chandra Tripathy

Erschienen in: Transactions on Rough Sets XXIII

Verlag: Springer Berlin Heidelberg

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Abstract

The main focus of this paper is to introduce some aggregation operators namely, Rough-Bipolar Neutrosophic Arithmetic Mean (RBNAM) operator and Rough-Bipolar Neutrosophic Geometric Mean (RBNGM) operator under Rough-Bipolar Neutrosophic Set (RBNS) environment. Besides, we present the concept of score and accuracy functions under the RBNS environment. Further, we propose two multi-attribute decision-making (MADM) strategies based on RBNAM operator and RBNGM operator respectively under the RBNS environment. Finally, we provide a real-life numerical example to validate the proposed MADM strategy.

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Vorheriges Kapitel Granularity and Rational Approximation: Rethinking Graded Rough Sets
Nächstes Kapitel Single-Valued Neutrosophic Rough Continuous Mapping via Single-Valued Neutrosophic Rough Topological Space
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Metadaten
Titel
MADM Strategies Based on Arithmetic and Geometric Mean Operator Under Rough-Bipolar Neutrosophic Set Environment
verfasst von
Surapati Pramanik
Suman Das
Rakhal Das
Binod Chandra Tripathy
Copyright-Jahr
2022
Verlag
Springer Berlin Heidelberg
Buch
Transactions on Rough Sets XXIII

Print ISBN: 978-3-662-66543-5

Electronic ISBN: 978-3-662-66544-2

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-662-66544-2_5

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