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Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain
The probabilistic hesitant fuzzy set (PHFS) is a useful extended version of the hesitant fuzzy set (HFS), which allows decision-makers greater freedom in espousing their preferences through the use of hesitant evidence in the real DM method. As the implications for individuals and global concerns ha...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868509/ https://www.ncbi.nlm.nih.gov/pubmed/36713935 http://dx.doi.org/10.1007/s11270-022-05970-6 |
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author | Jaisankar, Ramasamy Murugesan, Veeramuthu Narayanamoorthy, Samayan Ahmadian, Ali Suvitha, Krishnan Ferrara, Massimiliano Kang, Daekook |
author_facet | Jaisankar, Ramasamy Murugesan, Veeramuthu Narayanamoorthy, Samayan Ahmadian, Ali Suvitha, Krishnan Ferrara, Massimiliano Kang, Daekook |
author_sort | Jaisankar, Ramasamy |
collection | PubMed |
description | The probabilistic hesitant fuzzy set (PHFS) is a useful extended version of the hesitant fuzzy set (HFS), which allows decision-makers greater freedom in espousing their preferences through the use of hesitant evidence in the real DM method. As the implications for individuals and global concerns have grown, efficient clinical diagnosis of medical waste has been a major challenge, particularly in developing countries. Medical waste can be disposed of in a variety of ways. The essential thing is to decide which strategies work best. The optimal healthcare plastic waste disposal (HCPWD) option is a MCDM method involving a wide range of qualitative characteristics. The MCDM technique (ARAS) is then described, whereby the criterion weights are assessed using the recommended entropy weighted method (EWM) proportion and score function in order to increase the process utilisation. Moreover, the above-described approach is used to address a real-world problem by determining the optimal treatment option for healthcare waste (HCW) disposal. Finally, a feasibility analysis is given to support the stated viewpoint on HCPWD options being prioritised. |
format | Online Article Text |
id | pubmed-9868509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-98685092023-01-23 Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain Jaisankar, Ramasamy Murugesan, Veeramuthu Narayanamoorthy, Samayan Ahmadian, Ali Suvitha, Krishnan Ferrara, Massimiliano Kang, Daekook Water Air Soil Pollut Article The probabilistic hesitant fuzzy set (PHFS) is a useful extended version of the hesitant fuzzy set (HFS), which allows decision-makers greater freedom in espousing their preferences through the use of hesitant evidence in the real DM method. As the implications for individuals and global concerns have grown, efficient clinical diagnosis of medical waste has been a major challenge, particularly in developing countries. Medical waste can be disposed of in a variety of ways. The essential thing is to decide which strategies work best. The optimal healthcare plastic waste disposal (HCPWD) option is a MCDM method involving a wide range of qualitative characteristics. The MCDM technique (ARAS) is then described, whereby the criterion weights are assessed using the recommended entropy weighted method (EWM) proportion and score function in order to increase the process utilisation. Moreover, the above-described approach is used to address a real-world problem by determining the optimal treatment option for healthcare waste (HCW) disposal. Finally, a feasibility analysis is given to support the stated viewpoint on HCPWD options being prioritised. Springer International Publishing 2023-01-23 2023 /pmc/articles/PMC9868509/ /pubmed/36713935 http://dx.doi.org/10.1007/s11270-022-05970-6 Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Jaisankar, Ramasamy Murugesan, Veeramuthu Narayanamoorthy, Samayan Ahmadian, Ali Suvitha, Krishnan Ferrara, Massimiliano Kang, Daekook Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain |
title | Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain |
title_full | Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain |
title_fullStr | Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain |
title_full_unstemmed | Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain |
title_short | Integrated MCDM Approaches for Exploring the Ideal Therapeutic Plastic Disposal Technology: Probabilistic Hesitant Fuzzy Domain |
title_sort | integrated mcdm approaches for exploring the ideal therapeutic plastic disposal technology: probabilistic hesitant fuzzy domain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868509/ https://www.ncbi.nlm.nih.gov/pubmed/36713935 http://dx.doi.org/10.1007/s11270-022-05970-6 |
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