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Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model
Municipal solid waste (MSW) directly impacts community health and environmental degradation; therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 201...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444379/ https://www.ncbi.nlm.nih.gov/pubmed/34545265 http://dx.doi.org/10.1016/j.cie.2021.107668 |
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author | Govindan, Kannan Nasr, Arash Khalili Mostafazadeh, Parisa Mina, Hassan |
author_facet | Govindan, Kannan Nasr, Arash Khalili Mostafazadeh, Parisa Mina, Hassan |
author_sort | Govindan, Kannan |
collection | PubMed |
description | Municipal solid waste (MSW) directly impacts community health and environmental degradation; therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 2019 (COVID-19) are classified among infectious medical wastes; moreover, these wastes are hazardous because they threaten the environment and living organisms if they are not appropriately managed. This paper develops a bi-objective mixed-integer linear programming model for medical waste management during the COVID-19 outbreak. The proposed model minimizes the total costs and risks, simultaneously, of the population’s exposure to pollution. This paper considers some realistic assumptions for the first time, including location-routing problem, time window-based green vehicle routing problem, vehicles scheduling, vehicles failure, split delivery, population risk, and load-dependent fuel consumption to manage both infectious and non-infectious medical waste. We apply a fuzzy goal programming approach for solving the proposed bi-objective model, and the efficiency of the proposed model and solution approach is assessed using data related to 13 nodes of medical waste production in a location west of Tehran. |
format | Online Article Text |
id | pubmed-8444379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84443792021-09-16 Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model Govindan, Kannan Nasr, Arash Khalili Mostafazadeh, Parisa Mina, Hassan Comput Ind Eng Article Municipal solid waste (MSW) directly impacts community health and environmental degradation; therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 2019 (COVID-19) are classified among infectious medical wastes; moreover, these wastes are hazardous because they threaten the environment and living organisms if they are not appropriately managed. This paper develops a bi-objective mixed-integer linear programming model for medical waste management during the COVID-19 outbreak. The proposed model minimizes the total costs and risks, simultaneously, of the population’s exposure to pollution. This paper considers some realistic assumptions for the first time, including location-routing problem, time window-based green vehicle routing problem, vehicles scheduling, vehicles failure, split delivery, population risk, and load-dependent fuel consumption to manage both infectious and non-infectious medical waste. We apply a fuzzy goal programming approach for solving the proposed bi-objective model, and the efficiency of the proposed model and solution approach is assessed using data related to 13 nodes of medical waste production in a location west of Tehran. The Author(s). Published by Elsevier Ltd. 2021-12 2021-09-16 /pmc/articles/PMC8444379/ /pubmed/34545265 http://dx.doi.org/10.1016/j.cie.2021.107668 Text en © 2021 The Author(s) 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 Govindan, Kannan Nasr, Arash Khalili Mostafazadeh, Parisa Mina, Hassan Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model |
title | Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model |
title_full | Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model |
title_fullStr | Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model |
title_full_unstemmed | Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model |
title_short | Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model |
title_sort | medical waste management during coronavirus disease 2019 (covid-19) outbreak: a mathematical programming model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444379/ https://www.ncbi.nlm.nih.gov/pubmed/34545265 http://dx.doi.org/10.1016/j.cie.2021.107668 |
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