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Emergency decision support modeling under generalized spherical fuzzy Einstein aggregation information.

Authors :
Ashraf S
Abdullah S
Chinram R
Source :
Journal of ambient intelligence and humanized computing [J Ambient Intell Humaniz Comput] 2022; Vol. 13 (4), pp. 2091-2117. Date of Electronic Publication: 2021 Sep 25.
Publication Year :
2022

Abstract

Dominant emergency action should be adopted in the case of an emergency situation. Emergency is interpreted as limited time and information, harmfulness and uncertainty, and decision-makers are often critically bound by uncertainty and risk. This framework implements an emergency decision-making approach to address the emergency situation of COVID-19 in a spherical fuzzy environment. As the spherical fuzzy set (SFS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). Keeping in view the features of the SFSs, the purpose of this paper is to present some robust generalized operating laws in accordance with the Einstein norms. In addition, list of propose aggregation operators using Einstein operational laws under spherical fuzzy environment are developed. Furthermore, we design the algorithm based on the proposed aggregation operators to tackle the uncertainty in emergency decision making problems. Finally, numerical case study of COVID-19 as an emergency decision making is presented to demonstrate the applicability and validity of the proposed technique. Besides, the comparison of the existing and the proposed technique is established to show the effectiveness and validity of the established technique.<br />Competing Interests: Conflict of InterestThe authors declare that they have no conflict of interest regarding the publication of the research article.<br /> (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

Details

Language :
English
ISSN :
1868-5137
Volume :
13
Issue :
4
Database :
MEDLINE
Journal :
Journal of ambient intelligence and humanized computing
Publication Type :
Academic Journal
Accession number :
34603537
Full Text :
https://doi.org/10.1007/s12652-021-03493-2