A consensus-based Fermatean fuzzy WASPAS methodology for selection of healthcare waste treatment technology selection

Chandana Narasimha Rao; Matta Sujatha

doi:10.31181/dmame622023621

Authors

Chandana Narasimha Rao Department of Engineering Chemistry, College of Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-500302, Andhra Pradesh, India https://orcid.org/0000-0002-7574-7673
Matta Sujatha Department of Engineering Chemistry, College of Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-500302, Andhra Pradesh, India https://orcid.org/0000-0003-4408-7753

DOI:

https://doi.org/10.31181/dmame622023621

Keywords:

Fermatean fuzzy numbers, consensus reaching, WASPAS, healthcare waste treatment technology selection

Abstract

Healthcare waste (HCW) management is a complex issue influenced by many factors, including technological, economic, environmental, and social factors. It is possible to regard the evaluation of the best treatment technique for HCW management as a challenging case of MCDM (multi-criteria decision-making), where various alternatives and evaluation criteria must be considered. The presentation and handling of the shaky data are crucial to choosing the HCW treatment technology. In order to address the issue of MCDM issues with Fermatean fuzzy (FF) data, we first build a consensus-based WASPAS approach in this study. In the suggested integrated methodology, the rank of the alternatives is determined using the WASPAS method in an FF environment, and the attribute weights are estimated using the entropy measure technique. In the preceding, an HCW treatment technology assessment issue is considered to make the proposed structure's applicability more transparent. In this study, four HCW treatment methods—chemical disinfection, microwave disinfection, cremation, and autoclaving—are considered options. According to the study's findings, autoclaving is the most effective HCW treatment method. Additionally, we demonstrate a sensitivity assessment using several criteria weight sets to test the stability of our intriguing proposed approach. We also call attention to a contrast between our suggested approach to decision-making and the practices now in use.

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