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A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands
The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, including the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A Demand predic...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986146/ https://www.ncbi.nlm.nih.gov/pubmed/36909783 http://dx.doi.org/10.1016/j.tre.2023.103087 |
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author | Zhang, Jianghua Long, Daniel Zhuoyu Li, Yuchen |
author_facet | Zhang, Jianghua Long, Daniel Zhuoyu Li, Yuchen |
author_sort | Zhang, Jianghua |
collection | PubMed |
description | The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, including the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A Demand prediction-Network optimization-Decision adjustment framework is proposed for the emergency logistic network design. We first present an improved short-term epidemic model to predict the evolutionary trajectory of the epidemic. Then, considering the uncertainty of the estimated demands, we construct a capacitated multi-period, multi-echelon facility deployment and resource allocation robust optimization model to improve the reliability of the decisions. To address the conservativeness of robust solutions during the evolution of the epidemic, an uncertainty budget adjustment strategy is proposed and integrated into the rolling horizon optimization approach. The results of the case study show that (i) the short-term prediction method has higher accuracy and the accuracy increases with the amount of observed data; (ii) considering the demand uncertainty, the proposed robust optimization model combined with uncertainty budget adjustment strategy can improve the performance of the emergency logistic network; (iii) the proposed solution method is more efficient than its benchmark, especially for large-scale cases. Moreover, some managerial insights related to the emergency logistics network design problem are presented. |
format | Online Article Text |
id | pubmed-9986146 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99861462023-03-06 A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands Zhang, Jianghua Long, Daniel Zhuoyu Li, Yuchen Transp Res E Logist Transp Rev Article The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, including the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A Demand prediction-Network optimization-Decision adjustment framework is proposed for the emergency logistic network design. We first present an improved short-term epidemic model to predict the evolutionary trajectory of the epidemic. Then, considering the uncertainty of the estimated demands, we construct a capacitated multi-period, multi-echelon facility deployment and resource allocation robust optimization model to improve the reliability of the decisions. To address the conservativeness of robust solutions during the evolution of the epidemic, an uncertainty budget adjustment strategy is proposed and integrated into the rolling horizon optimization approach. The results of the case study show that (i) the short-term prediction method has higher accuracy and the accuracy increases with the amount of observed data; (ii) considering the demand uncertainty, the proposed robust optimization model combined with uncertainty budget adjustment strategy can improve the performance of the emergency logistic network; (iii) the proposed solution method is more efficient than its benchmark, especially for large-scale cases. Moreover, some managerial insights related to the emergency logistics network design problem are presented. Elsevier Ltd. 2023-04 2023-03-06 /pmc/articles/PMC9986146/ /pubmed/36909783 http://dx.doi.org/10.1016/j.tre.2023.103087 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 Zhang, Jianghua Long, Daniel Zhuoyu Li, Yuchen A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands |
title | A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands |
title_full | A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands |
title_fullStr | A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands |
title_full_unstemmed | A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands |
title_short | A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands |
title_sort | reliable emergency logistics network for covid-19 considering the uncertain time-varying demands |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986146/ https://www.ncbi.nlm.nih.gov/pubmed/36909783 http://dx.doi.org/10.1016/j.tre.2023.103087 |
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