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Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function

The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management treatment needs to be re-taken into focus to deal with this challenge. In practice, there are several treatments of HCW with their advantages and disadvantages. This study is conducted to select the appropriate treatm...

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Autores principales: Pamučar, Dragan, Puška, Adis, Simić, Vladimir, Stojanović, Ilija, Deveci, Muhammet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9985309/
https://www.ncbi.nlm.nih.gov/pubmed/36908983
http://dx.doi.org/10.1016/j.engappai.2023.106025
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author Pamučar, Dragan
Puška, Adis
Simić, Vladimir
Stojanović, Ilija
Deveci, Muhammet
author_facet Pamučar, Dragan
Puška, Adis
Simić, Vladimir
Stojanović, Ilija
Deveci, Muhammet
author_sort Pamučar, Dragan
collection PubMed
description The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management treatment needs to be re-taken into focus to deal with this challenge. In practice, there are several treatments of HCW with their advantages and disadvantages. This study is conducted to select the appropriate treatment for HCW in the Brčko District of Bosnia and Herzegovina. Six HCW management treatments are analyzed and observed through twelve criteria. Ten-level linguistic values were used to bring this evaluation closer to human thinking. A fuzzy rough approach is used to solve the problem of inaccuracy in determining these values. The OPA method from the Bonferroni operator is used to determine the weights of the criteria. The results of the application of this method showed that the criterion Environmental Impact ([Formula: see text]) received the highest weight, while the criterion Automation Level ([Formula: see text]) received the lowest value. The ranking of HCW management treatments was performed using MARCOS methods based on the Aczel–Alsina function. The results of this analysis showed that the best-ranked HCW management treatment is microwave (A6) while landfill treatment (A5) is ranked worst. This study has provided a new approach based on fuzzy rough numbers where the Bonferroni function is used to determine the lower and upper limits, while the application of the Aczel–Alsina function reduced the influence of decision-makers on the final decision because this function stabilizes the decision-making process.
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spelling pubmed-99853092023-03-06 Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function Pamučar, Dragan Puška, Adis Simić, Vladimir Stojanović, Ilija Deveci, Muhammet Eng Appl Artif Intell Article The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management treatment needs to be re-taken into focus to deal with this challenge. In practice, there are several treatments of HCW with their advantages and disadvantages. This study is conducted to select the appropriate treatment for HCW in the Brčko District of Bosnia and Herzegovina. Six HCW management treatments are analyzed and observed through twelve criteria. Ten-level linguistic values were used to bring this evaluation closer to human thinking. A fuzzy rough approach is used to solve the problem of inaccuracy in determining these values. The OPA method from the Bonferroni operator is used to determine the weights of the criteria. The results of the application of this method showed that the criterion Environmental Impact ([Formula: see text]) received the highest weight, while the criterion Automation Level ([Formula: see text]) received the lowest value. The ranking of HCW management treatments was performed using MARCOS methods based on the Aczel–Alsina function. The results of this analysis showed that the best-ranked HCW management treatment is microwave (A6) while landfill treatment (A5) is ranked worst. This study has provided a new approach based on fuzzy rough numbers where the Bonferroni function is used to determine the lower and upper limits, while the application of the Aczel–Alsina function reduced the influence of decision-makers on the final decision because this function stabilizes the decision-making process. Elsevier Ltd. 2023-05 2023-03-04 /pmc/articles/PMC9985309/ /pubmed/36908983 http://dx.doi.org/10.1016/j.engappai.2023.106025 Text en © 2023 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Pamučar, Dragan
Puška, Adis
Simić, Vladimir
Stojanović, Ilija
Deveci, Muhammet
Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function
title Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function
title_full Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function
title_fullStr Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function
title_full_unstemmed Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function
title_short Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function
title_sort selection of healthcare waste management treatment using fuzzy rough numbers and aczel–alsina function
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9985309/
https://www.ncbi.nlm.nih.gov/pubmed/36908983
http://dx.doi.org/10.1016/j.engappai.2023.106025
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