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Research Article | Open Access

q-Rung orthopair fuzzy information aggregation and their application towards material selection

Adel Fahad Alrasheedi1Jungeun Kim2( )Rukhsana Kausar3
Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Department of Software and CMPSI, Kongju National University, Cheonan 31080, South Korea
Department of Mathematics, University of the Punjab, Lahore, Pakistan
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Abstract

Material selection is a complex process that involves selecting the best material for a given application. It is a critical process in engineering, and the importance of selecting the right material for the job cannot be overstated. Multi-criteria decision-making (MCDM) is an important tool that can be used to help engineers make informed decisions about material selection. The logistic function can be extended using the soft-max function, which is widely used in stochastic classification methods like neural nets, soft-max extrapolation, linear differential analysis, and Naïve Bayes detectors. This has inspired researchers to develop soft-max-based fuzzy aggregation operators (AOs) for q-rung orthopair fuzzy sets (q-ROPFS) and to propose an MCDM approach based on these AOs. To test the effectiveness of this approach, the researchers applied it to a practical problem using q-rung orthopair fuzzy data and conducted a numerical example to validate the suggested procedures.

CLC number: 03E72, 94D05, 90B50

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AIMS Mathematics
Pages 18780-18808

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Cite this article:
Alrasheedi AF, Kim J, Kausar R. q-Rung orthopair fuzzy information aggregation and their application towards material selection. AIMS Mathematics, 2023, 8(8): 18780-18808. https://doi.org/10.3934/math.2023956

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Received: 16 March 2023
Revised: 17 May 2023
Accepted: 22 May 2023
Published: 15 August 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)