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Research Article | Open Access

Analysis of Einstein aggregation operators based on complex intuitionistic fuzzy sets and their applications in multi-attribute decision-making

Wajid Azeem1Waqas Mahmood1Tahir Mahmood2( )Zeeshan Ali2Muhammad Naeem3( )
Department of Mathematics, Quaid-i-Azam University, Islamabad 44000, Pakistan
Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan
Department of Mathematics, Deanship of Applied Sciences Umm Al-Qura University, Makkah, Saudi Arabia
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Abstract

The main influence of this analysis is to derive two different types of aggregation operators under the consideration of algebraic t-norm and t-conorm and Einstein t-norm and t-conorm for CIF set theory. Because these operators are very effective for evaluating the collection of information into a singleton preference. For this, first, we discover the Algebraic and Einstein operational laws for CIF sets. Then, we aim to discover the theory of CCIFWA, CCIFOWA, CCIFWG, CCIFOWG operators and their valuable properties "idempotency, monotonicity and boundedness" and results. Furthermore, we also derive the theory of CCIFEWA, CCIFEOWA, CCIFEWG, CCIFEOWG operators and their valuable properties "idempotency, monotonicity, and boundedness" and results. Some special cases of the derived work are also described in detail. Finally, we illustrate a MADM procedure under the consideration of derived operators to enhance the worth of the presented information. Finally, we compare the presented operators with various existing operators with the help of various suitable examples for showing the reliability and stability of the derived approaches.

CLC number: 03B52, 68T27, 68T37, 94D05, 03E72

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AIMS Mathematics
Pages 6036-6063

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Cite this article:
Azeem W, Mahmood W, Mahmood T, et al. Analysis of Einstein aggregation operators based on complex intuitionistic fuzzy sets and their applications in multi-attribute decision-making. AIMS Mathematics, 2023, 8(3): 6036-6063. https://doi.org/10.3934/math.2023305

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Received: 24 October 2022
Revised: 07 December 2022
Accepted: 19 December 2022
Published: 15 March 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)