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

Shahzaib Ashraf1, Saleem Abdullah2, Ronnason Chinram3

  • 1Department of Mathematics and Statistics, Bacha Khan University, Charsadda, 24420 Khyber Pakhtunkhwa, Pakistan.

Journal of Ambient Intelligence and Humanized Computing
|October 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel decision-making framework using spherical fuzzy sets (SFS) to manage uncertainty during emergencies like COVID-19. The approach enhances emergency response by developing new aggregation operators for better decision-making under risk.

Keywords:
COVID-19Emergency decision making techniqueGeneralized Einstein aggregation operatorsSpherical fuzzy sets

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Area of Science:

  • Decision Sciences
  • Artificial Intelligence
  • Public Health

Background:

  • Emergency situations are characterized by time constraints, limited information, and high uncertainty, complicating decision-making.
  • Traditional decision-making models struggle with the ambiguity inherent in critical events.
  • Spherical Fuzzy Sets (SFS) offer a generalized framework to effectively handle complex uncertainties in decision-making problems.

Purpose of the Study:

  • To introduce robust generalized operating laws based on Einstein norms within a spherical fuzzy environment.
  • To develop novel aggregation operators utilizing these Einstein operational laws for spherical fuzzy sets.
  • To design an algorithm employing these operators to address uncertainty in emergency decision-making.

Main Methods:

  • Development of new Einstein operational laws for spherical fuzzy sets.
  • Creation of aggregation operators based on the proposed Einstein norms.
  • Design of a decision-making algorithm incorporating these operators.
  • Application of the framework to a COVID-19 case study.

Main Results:

  • The proposed Einstein operational laws and aggregation operators effectively handle uncertainty in spherical fuzzy environments.
  • The developed algorithm demonstrates applicability and validity in real-world emergency scenarios.
  • A comparative analysis confirms the superiority of the proposed technique over existing methods for emergency decision-making.

Conclusions:

  • The presented framework provides a robust approach for emergency decision-making, particularly in complex situations like pandemics.
  • The use of spherical fuzzy sets and Einstein norms enhances the ability to manage uncertainty and risk.
  • The study validates the effectiveness of the proposed technique through a COVID-19 case study, offering a valuable tool for future emergency management.