Application of decomposition techniques in a wildfire suppression optimization model

Resource assignment and scheduling models provides an automatic and fast decision support system for wildfire suppression logistics. However, this process generates challenging optimization problems in many real-world cases, and the computational time becomes a critical issue, especially in realisti...

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Detalhes bibliográficos
Autores: Rodríguez-Veiga, J., Penas, David R., González-Rueda, A. M., Ginzo-Villamayor, M.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/345858
Acesso em linha:http://hdl.handle.net/10261/345858
Access Level:acceso abierto
Palavra-chave:Integer programming
Assignment problems
Wildfire management
Decomposition techniques
Benders decomposition
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spelling Application of decomposition techniques in a wildfire suppression optimization modelRodríguez-Veiga, J.Penas, David R.González-Rueda, A. M.Ginzo-Villamayor, M.Integer programmingAssignment problemsWildfire managementDecomposition techniquesBenders decompositionResource assignment and scheduling models provides an automatic and fast decision support system for wildfire suppression logistics. However, this process generates challenging optimization problems in many real-world cases, and the computational time becomes a critical issue, especially in realistic-size instances. Thus, to overcome that limitation, this work studies and applies a set of decomposition techniques such as augmented Lagrangian, branch and price, and Benders decomposition’s to a wildfire suppression model. Moreover, a reformulation strategy, inspired by Benders’ decomposition, is also introduced and demonstrated. Finally, a numerical study comparing the behavior of the proposals using different problem sizes is conducted.This research work is supported by the R+D+I project grants PID2020-116587GB-I00 and PID2021-124030NB (C31 and C32), funded by MCIN/AEI/10.13039/501100011033/ and by “ERDF A way of making Europe”/EU. Second author investigation is funded by the Xunta de Galicia (contract post-doctoral 2019-2022). We acknowledge the computational resources provided by CESGA. Third author acknowledges support from the Xunta de Galicia through the ERDF (ED431C-2020-14 and ED431G 2019/01), and “CITIC”. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Kluwer Academic PublishersConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2024202420232024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/345858reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116587GB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-124030NB-C31info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-124030NB-C32http://dx.doi.org/10.1007/s11081-022-09783-8Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3458582026-05-22T06:33:51Z
dc.title.none.fl_str_mv Application of decomposition techniques in a wildfire suppression optimization model
title Application of decomposition techniques in a wildfire suppression optimization model
spellingShingle Application of decomposition techniques in a wildfire suppression optimization model
Rodríguez-Veiga, J.
Integer programming
Assignment problems
Wildfire management
Decomposition techniques
Benders decomposition
title_short Application of decomposition techniques in a wildfire suppression optimization model
title_full Application of decomposition techniques in a wildfire suppression optimization model
title_fullStr Application of decomposition techniques in a wildfire suppression optimization model
title_full_unstemmed Application of decomposition techniques in a wildfire suppression optimization model
title_sort Application of decomposition techniques in a wildfire suppression optimization model
dc.creator.none.fl_str_mv Rodríguez-Veiga, J.
Penas, David R.
González-Rueda, A. M.
Ginzo-Villamayor, M.
author Rodríguez-Veiga, J.
author_facet Rodríguez-Veiga, J.
Penas, David R.
González-Rueda, A. M.
Ginzo-Villamayor, M.
author_role author
author2 Penas, David R.
González-Rueda, A. M.
Ginzo-Villamayor, M.
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Integer programming
Assignment problems
Wildfire management
Decomposition techniques
Benders decomposition
topic Integer programming
Assignment problems
Wildfire management
Decomposition techniques
Benders decomposition
description Resource assignment and scheduling models provides an automatic and fast decision support system for wildfire suppression logistics. However, this process generates challenging optimization problems in many real-world cases, and the computational time becomes a critical issue, especially in realistic-size instances. Thus, to overcome that limitation, this work studies and applies a set of decomposition techniques such as augmented Lagrangian, branch and price, and Benders decomposition’s to a wildfire suppression model. Moreover, a reformulation strategy, inspired by Benders’ decomposition, is also introduced and demonstrated. Finally, a numerical study comparing the behavior of the proposals using different problem sizes is conducted.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/345858
url http://hdl.handle.net/10261/345858
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116587GB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-124030NB-C31
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-124030NB-C32
http://dx.doi.org/10.1007/s11081-022-09783-8
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Kluwer Academic Publishers
publisher.none.fl_str_mv Kluwer Academic Publishers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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