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Robust optimization approach to emergency mobile facility routing

Emergency events such as natural disasters, environmental events, sudden illness, and social security events pose tremendous threats to people’s lives and property security. In order to meet emergency service demands by rationally allocating mobile facilities, an emergency mobile facility routing mo...

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Detalles Bibliográficos
Autores principales: Li, Jianxun, Lai, Kin Keung, Fu, Yelin, Shen, Hai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454993/
https://www.ncbi.nlm.nih.gov/pubmed/33423609
http://dx.doi.org/10.1177/0036850420982685
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author Li, Jianxun
Lai, Kin Keung
Fu, Yelin
Shen, Hai
author_facet Li, Jianxun
Lai, Kin Keung
Fu, Yelin
Shen, Hai
author_sort Li, Jianxun
collection PubMed
description Emergency events such as natural disasters, environmental events, sudden illness, and social security events pose tremendous threats to people’s lives and property security. In order to meet emergency service demands by rationally allocating mobile facilities, an emergency mobile facility routing model is proposed to maximize the total served demand by the available mobile facilities. Based on the uninterruptible feature of emergency services, the model abstracts emergency events act as a combination of multiple uncertain variables. To overcome the computational difficulty, a robust optimization approach and genetic algorithm are employed to obtain solutions. Illustrative examples show that it provides an effective method for solving the emergency mobile facility routing problem, and that the risk factor and penalty factor of the model can further guide decision-making.
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spelling pubmed-104549932023-08-26 Robust optimization approach to emergency mobile facility routing Li, Jianxun Lai, Kin Keung Fu, Yelin Shen, Hai Sci Prog Article Emergency events such as natural disasters, environmental events, sudden illness, and social security events pose tremendous threats to people’s lives and property security. In order to meet emergency service demands by rationally allocating mobile facilities, an emergency mobile facility routing model is proposed to maximize the total served demand by the available mobile facilities. Based on the uninterruptible feature of emergency services, the model abstracts emergency events act as a combination of multiple uncertain variables. To overcome the computational difficulty, a robust optimization approach and genetic algorithm are employed to obtain solutions. Illustrative examples show that it provides an effective method for solving the emergency mobile facility routing problem, and that the risk factor and penalty factor of the model can further guide decision-making. SAGE Publications 2021-01-11 /pmc/articles/PMC10454993/ /pubmed/33423609 http://dx.doi.org/10.1177/0036850420982685 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Article
Li, Jianxun
Lai, Kin Keung
Fu, Yelin
Shen, Hai
Robust optimization approach to emergency mobile facility routing
title Robust optimization approach to emergency mobile facility routing
title_full Robust optimization approach to emergency mobile facility routing
title_fullStr Robust optimization approach to emergency mobile facility routing
title_full_unstemmed Robust optimization approach to emergency mobile facility routing
title_short Robust optimization approach to emergency mobile facility routing
title_sort robust optimization approach to emergency mobile facility routing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454993/
https://www.ncbi.nlm.nih.gov/pubmed/33423609
http://dx.doi.org/10.1177/0036850420982685
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