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Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method

Medical services inevitably generate healthcare waste (HCW) that may become hazardous to healthcare staffs, patients, the population, and the atmosphere. In most of the developing countries, HCW disposal management has become one of the fastest-growing challenges for urban municipalities and healthc...

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Autores principales: Mishra, Arunodaya Raj, Rani, Pratibha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212908/
https://www.ncbi.nlm.nih.gov/pubmed/34777968
http://dx.doi.org/10.1007/s40747-021-00407-9
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author Mishra, Arunodaya Raj
Rani, Pratibha
author_facet Mishra, Arunodaya Raj
Rani, Pratibha
author_sort Mishra, Arunodaya Raj
collection PubMed
description Medical services inevitably generate healthcare waste (HCW) that may become hazardous to healthcare staffs, patients, the population, and the atmosphere. In most of the developing countries, HCW disposal management has become one of the fastest-growing challenges for urban municipalities and healthcare providers. Determining the location for HCW disposal centers is a relatively complex process due to the involvement of various alternatives, criteria, and strict government guidelines about the disposal of HCW. The objective of the paper is to introduce the WASPAS (weighted aggregated sum product assessment) method with Fermatean fuzzy sets (FFSs) for the HCW disposal location selection problem. This method combines the score function, entropy measure, and classical WASPAS approach within FFSs context. Next, a combined procedure using entropy and score function is proposed to estimate the criteria weights. To do this, a novel score function with its desirable properties and some entropy measures are introduced under the FFSs context. Further, an illustrative case study of the HCW disposal location selection problem on FFSs is established, which evidences the practicality and efficacy of the developed approach. Comparative discussion and sensitivity analysis are made to monitor the permanence of the introduced framework. The final results approve that the proposed methodology can effectively handle the ambiguity and inaccuracy in the decision-making procedure of HCW disposal location selection.
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spelling pubmed-82129082021-06-21 Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method Mishra, Arunodaya Raj Rani, Pratibha Complex Intell Systems Original Article Medical services inevitably generate healthcare waste (HCW) that may become hazardous to healthcare staffs, patients, the population, and the atmosphere. In most of the developing countries, HCW disposal management has become one of the fastest-growing challenges for urban municipalities and healthcare providers. Determining the location for HCW disposal centers is a relatively complex process due to the involvement of various alternatives, criteria, and strict government guidelines about the disposal of HCW. The objective of the paper is to introduce the WASPAS (weighted aggregated sum product assessment) method with Fermatean fuzzy sets (FFSs) for the HCW disposal location selection problem. This method combines the score function, entropy measure, and classical WASPAS approach within FFSs context. Next, a combined procedure using entropy and score function is proposed to estimate the criteria weights. To do this, a novel score function with its desirable properties and some entropy measures are introduced under the FFSs context. Further, an illustrative case study of the HCW disposal location selection problem on FFSs is established, which evidences the practicality and efficacy of the developed approach. Comparative discussion and sensitivity analysis are made to monitor the permanence of the introduced framework. The final results approve that the proposed methodology can effectively handle the ambiguity and inaccuracy in the decision-making procedure of HCW disposal location selection. Springer International Publishing 2021-06-18 2021 /pmc/articles/PMC8212908/ /pubmed/34777968 http://dx.doi.org/10.1007/s40747-021-00407-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Mishra, Arunodaya Raj
Rani, Pratibha
Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method
title Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method
title_full Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method
title_fullStr Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method
title_full_unstemmed Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method
title_short Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method
title_sort multi-criteria healthcare waste disposal location selection based on fermatean fuzzy waspas method
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212908/
https://www.ncbi.nlm.nih.gov/pubmed/34777968
http://dx.doi.org/10.1007/s40747-021-00407-9
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