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Reliable Facility Location Problem with Facility Protection
This paper studies a reliable facility location problem with facility protection that aims to hedge against random facility disruptions by both strategically protecting some facilities and using backup facilities for the demands. An Integer Programming model is proposed for this problem, in which th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008800/ https://www.ncbi.nlm.nih.gov/pubmed/27583542 http://dx.doi.org/10.1371/journal.pone.0161532 |
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author | Tang, Luohao Zhu, Cheng Lin, Zaili Shi, Jianmai Zhang, Weiming |
author_facet | Tang, Luohao Zhu, Cheng Lin, Zaili Shi, Jianmai Zhang, Weiming |
author_sort | Tang, Luohao |
collection | PubMed |
description | This paper studies a reliable facility location problem with facility protection that aims to hedge against random facility disruptions by both strategically protecting some facilities and using backup facilities for the demands. An Integer Programming model is proposed for this problem, in which the failure probabilities of facilities are site-specific. A solution approach combining Lagrangian Relaxation and local search is proposed and is demonstrated to be both effective and efficient based on computational experiments on random numerical examples with 49, 88, 150 and 263 nodes in the network. A real case study for a 100-city network in Hunan province, China, is presented, based on which the properties of the model are discussed and some managerial insights are analyzed. |
format | Online Article Text |
id | pubmed-5008800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50088002016-09-27 Reliable Facility Location Problem with Facility Protection Tang, Luohao Zhu, Cheng Lin, Zaili Shi, Jianmai Zhang, Weiming PLoS One Research Article This paper studies a reliable facility location problem with facility protection that aims to hedge against random facility disruptions by both strategically protecting some facilities and using backup facilities for the demands. An Integer Programming model is proposed for this problem, in which the failure probabilities of facilities are site-specific. A solution approach combining Lagrangian Relaxation and local search is proposed and is demonstrated to be both effective and efficient based on computational experiments on random numerical examples with 49, 88, 150 and 263 nodes in the network. A real case study for a 100-city network in Hunan province, China, is presented, based on which the properties of the model are discussed and some managerial insights are analyzed. Public Library of Science 2016-09-01 /pmc/articles/PMC5008800/ /pubmed/27583542 http://dx.doi.org/10.1371/journal.pone.0161532 Text en © 2016 Tang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tang, Luohao Zhu, Cheng Lin, Zaili Shi, Jianmai Zhang, Weiming Reliable Facility Location Problem with Facility Protection |
title | Reliable Facility Location Problem with Facility Protection |
title_full | Reliable Facility Location Problem with Facility Protection |
title_fullStr | Reliable Facility Location Problem with Facility Protection |
title_full_unstemmed | Reliable Facility Location Problem with Facility Protection |
title_short | Reliable Facility Location Problem with Facility Protection |
title_sort | reliable facility location problem with facility protection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008800/ https://www.ncbi.nlm.nih.gov/pubmed/27583542 http://dx.doi.org/10.1371/journal.pone.0161532 |
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