Emergency decision support modeling for COVID‐19 based on spherical fuzzy information

International Journal of Intelligent SystemsVolume 35, Issue 11 p. 1601-1645 RESEARCH ARTICLE Emergency decision support modeling for COVID-19 based on spherical fuzzy information Shahzaib Ashraf, Corresponding Author Shahzaib Ashraf [email protected] Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan Correspondence Shahzaib Ashraf and Saleem Abdullah, Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan. Email: [email protected] (SA); [email protected] (SA)Search for more papers by this authorSaleem Abdullah, Corresponding Author Saleem Abdullah [email protected] Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan Correspondence Shahzaib Ashraf and Saleem Abdullah, Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan. Email: [email protected] (SA); [email protected] (SA)Search for more papers by this author Shahzaib Ashraf, Corresponding Author Shahzaib Ashraf [email protected] Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan Correspondence Shahzaib Ashraf and Saleem Abdullah, Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan. Email: [email protected] (SA); [email protected] (SA)Search for more papers by this authorSaleem Abdullah, Corresponding Author Saleem Abdullah [email protected] Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan Correspondence Shahzaib Ashraf and Saleem Abdullah, Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan. Email: [email protected] (SA); [email protected] (SA)Search for more papers by this author First published: 18 August 2020 https://doi.org/10.1002/int.22262Citations: 17Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19. Citing Literature Volume35, Issue11November 2020Pages 1601-1645 RelatedInformation