Robust Critical Node Selection by Benders Decomposition

The critical node selection problem (CNP) has important applications in telecommunication, supply chain design, and disease propagation prevention. In practice, the weights on the connections are often uncertain or hard to estimate. For this reason, robust optimization approaches have been considere...

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Detalhes bibliográficos
Autores: Naoum-Sawaya, Joe, Buchheim, Christoph
Formato: artículo
Fecha de publicación:2016
País:España
Recursos:IE
Repositorio:Repositorio IE
OAI Identifier:oai:repositorio.ie.edu:20.500.14417/4119
Acesso em linha:https://doi.org/10.1287/ijoc.2015.0671
https://hdl.handle.net/20.500.14417/4119
https://pubsonline.informs.org/doi/10.1287/ijoc.2015.0671
Access Level:acceso abierto
Palavra-chave:33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
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spelling Robust Critical Node Selection by Benders DecompositionNaoum-Sawaya, JoeBuchheim, Christoph33 Ciencias TecnológicasODS 9 - Industria, innovación e infraestructuraThe critical node selection problem (CNP) has important applications in telecommunication, supply chain design, and disease propagation prevention. In practice, the weights on the connections are often uncertain or hard to estimate. For this reason, robust optimization approaches have been considered recently for CNP. In this article, we address very general uncertainty sets, only requiring a linear optimization oracle for the set of potential scenarios. In particular, we can deal with discrete scenario based uncertainty, gamma uncertainty, and ellipsoidal uncertainty. For this general class of robust critical node selection problems, we propose an exact solution method based on Benders decomposition. The Benders subproblem, which in our approach is a robust optimization problem, is efficiently solved by applying the Floyd-Warshall algorithm. The presented approach is tested on 384 instances based on Forest-Fire, Barabási-Albert, Erdős-Rényi, and Watts-Strogatz graphs with different number of nodes and edges, where running times are compared to CPLEX being directly applied to the robust problem formulation. The computational results show the advantage of the proposed approach in handling the uncertainty thus outperforming CPLEX most notably for the ellipsoidal uncertainty cases.YesPublishedInstitute for Operations Research and Management Scienceshttps://ror.org/02jjdwm7520262016info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1287/ijoc.2015.0671https://hdl.handle.net/20.500.14417/4119https://pubsonline.informs.org/doi/10.1287/ijoc.2015.0671reponame:Repositorio IEinstname:IEInglésIE School of Science & TechnologyIE UniversityAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositorio.ie.edu:20.500.14417/41192026-06-15T12:40:57Z
dc.title.none.fl_str_mv Robust Critical Node Selection by Benders Decomposition
title Robust Critical Node Selection by Benders Decomposition
spellingShingle Robust Critical Node Selection by Benders Decomposition
Naoum-Sawaya, Joe
33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
title_short Robust Critical Node Selection by Benders Decomposition
title_full Robust Critical Node Selection by Benders Decomposition
title_fullStr Robust Critical Node Selection by Benders Decomposition
title_full_unstemmed Robust Critical Node Selection by Benders Decomposition
title_sort Robust Critical Node Selection by Benders Decomposition
dc.creator.none.fl_str_mv Naoum-Sawaya, Joe
Buchheim, Christoph
author Naoum-Sawaya, Joe
author_facet Naoum-Sawaya, Joe
Buchheim, Christoph
author_role author
author2 Buchheim, Christoph
author2_role author
dc.contributor.none.fl_str_mv https://ror.org/02jjdwm75
dc.subject.none.fl_str_mv 33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
topic 33 Ciencias Tecnológicas
ODS 9 - Industria, innovación e infraestructura
description The critical node selection problem (CNP) has important applications in telecommunication, supply chain design, and disease propagation prevention. In practice, the weights on the connections are often uncertain or hard to estimate. For this reason, robust optimization approaches have been considered recently for CNP. In this article, we address very general uncertainty sets, only requiring a linear optimization oracle for the set of potential scenarios. In particular, we can deal with discrete scenario based uncertainty, gamma uncertainty, and ellipsoidal uncertainty. For this general class of robust critical node selection problems, we propose an exact solution method based on Benders decomposition. The Benders subproblem, which in our approach is a robust optimization problem, is efficiently solved by applying the Floyd-Warshall algorithm. The presented approach is tested on 384 instances based on Forest-Fire, Barabási-Albert, Erdős-Rényi, and Watts-Strogatz graphs with different number of nodes and edges, where running times are compared to CPLEX being directly applied to the robust problem formulation. The computational results show the advantage of the proposed approach in handling the uncertainty thus outperforming CPLEX most notably for the ellipsoidal uncertainty cases.
publishDate 2016
dc.date.none.fl_str_mv 2016
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1287/ijoc.2015.0671
https://hdl.handle.net/20.500.14417/4119
https://pubsonline.informs.org/doi/10.1287/ijoc.2015.0671
url https://doi.org/10.1287/ijoc.2015.0671
https://hdl.handle.net/20.500.14417/4119
https://pubsonline.informs.org/doi/10.1287/ijoc.2015.0671
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IE School of Science & Technology
IE University
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute for Operations Research and Management Sciences
publisher.none.fl_str_mv Institute for Operations Research and Management Sciences
dc.source.none.fl_str_mv reponame:Repositorio IE
instname:IE
instname_str IE
reponame_str Repositorio IE
collection Repositorio IE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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