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A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment

Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timel...

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Detalles Bibliográficos
Autores principales: Liang, Bian, Yang, Dapeng, Qin, Xinghong, Tinta, Teresa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843944/
https://www.ncbi.nlm.nih.gov/pubmed/31635076
http://dx.doi.org/10.3390/ijerph16204007
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author Liang, Bian
Yang, Dapeng
Qin, Xinghong
Tinta, Teresa
author_facet Liang, Bian
Yang, Dapeng
Qin, Xinghong
Tinta, Teresa
author_sort Liang, Bian
collection PubMed
description Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.
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spelling pubmed-68439442019-11-25 A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment Liang, Bian Yang, Dapeng Qin, Xinghong Tinta, Teresa Int J Environ Res Public Health Article Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia. MDPI 2019-10-19 2019-10 /pmc/articles/PMC6843944/ /pubmed/31635076 http://dx.doi.org/10.3390/ijerph16204007 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liang, Bian
Yang, Dapeng
Qin, Xinghong
Tinta, Teresa
A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment
title A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment
title_full A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment
title_fullStr A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment
title_full_unstemmed A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment
title_short A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment
title_sort risk-averse shelter location and evacuation routing assignment problem in an uncertain environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843944/
https://www.ncbi.nlm.nih.gov/pubmed/31635076
http://dx.doi.org/10.3390/ijerph16204007
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