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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...

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Detalles Bibliográficos
Autores principales: Zhang, Jianghua, Long, Daniel Zhuoyu, Li, Yuchen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2023
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.
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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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