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Spherical uncertain linguistic Hamacher aggregation operators and their application on achieving consistent opinion fusion in group decision making

Muhammad Qiyas (Mathematics, Abdul Wali Khan University, Mardan, Pakistan)
Saleem Abdullah (Abdul Wali Khan University, Mardan, Pakistan)
Muhammad Naeem (Department of Preparatory Year, Umm Al-Qura University, Makkah, Saudi Arabia)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 20 July 2021

Issue publication date: 4 October 2021

116

Abstract

Purpose

The aim of this research is to establish a new type of aggregation operator based on Hamacher operational law of spherical uncertain linguistic numbers (SULNs).

Design/methodology/approach

First, the authors define spherical uncertain linguistic sets and develop some operational laws of SULNs. Furthermore, the authors extended these operational laws to the aggregation operator and developed spherical uncertain linguistic Hamacher averaging and geometric aggregation operators.

Findings

The authors were limited in achieving a consistent opinion on the fusion in group decision-making problem with the SULN information.

Originality/value

In order to give an application of the introduced operators, the authors first constrict a system of multi-attribute decision-making algorithm.

Keywords

Acknowledgements

The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by grant number 19-SCI-101-0056.

Conflicts of interest: The authors declare that they have no conflicts of interest.

Citation

Qiyas, M., Abdullah, S. and Naeem, M. (2021), "Spherical uncertain linguistic Hamacher aggregation operators and their application on achieving consistent opinion fusion in group decision making", International Journal of Intelligent Computing and Cybernetics, Vol. 14 No. 4, pp. 550-579. https://doi.org/10.1108/IJICC-09-2020-0120

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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