SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization

Iterated Local Search (ILS) is one of the most popular single-solution-based metaheuristics. ILS is recognized by many authors as a relatively simple yet efficient framework able to deal with complex combinatorial optimization problems (COPs). ILS-based algorithms have been successfully applied to p...

ver descrição completa

Detalhes bibliográficos
Autores: Grasas, Àlex, Juan, Angel A., Ramalhinho-Lourenço, Helena
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2014
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/44209
Acesso em linha:http://hdl.handle.net/10230/44209
http://dx.doi.org/10.1057/jos.2014.25
Access Level:acceso abierto
Palavra-chave:Iterated local search
Simulation
Stochastic combinatorial optimization
Simheuristics
id ES_0a2a7b455be11684f91767fc449cf412
oai_identifier_str oai:recercat.cat:10230/44209
network_acronym_str ES
network_name_str España
repository_id_str
spelling SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimizationGrasas, ÀlexJuan, Angel A.Ramalhinho-Lourenço, HelenaIterated local searchSimulationStochastic combinatorial optimizationSimheuristicsIterated Local Search (ILS) is one of the most popular single-solution-based metaheuristics. ILS is recognized by many authors as a relatively simple yet efficient framework able to deal with complex combinatorial optimization problems (COPs). ILS-based algorithms have been successfully applied to provide near-optimal solutions to different COPs in logistics, transportation, production, etc. However, ILS is designed to solve COPs under deterministic scenarios. In some real-life applications where uncertainty is present, the deterministic assumption makes the model less accurate since it does not reflect the real stochastic nature of the system. This paper presents the SimILS framework that extends ILS by integrating simulation to be able to cope with Stochastic COPs in a natural way. The paper also describes several tested applications that illustrate the main concepts behind SimILS and give rise to a new brand of ILS-based algorithms.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P) and by the Ibero-American Programme for Science, Technology and Development (CYTED2010-511RT0419).Taylor & Francis202020202014info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/44209http://dx.doi.org/10.1057/jos.2014.25reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésJournal of Simulation. 2017 Oct 3;10(1):69-77info:eu-repo/grantAgreement/ES/1PE/TRA2013-48180-C3-P© This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Simulation on 2014 Oct 3, available online: http://www.tandfonline.com/10.1057/jos.2014.25info:eu-repo/semantics/openAccessoai:recercat.cat:10230/442092026-05-29T05:05:01Z
dc.title.none.fl_str_mv SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
title SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
spellingShingle SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
Grasas, Àlex
Iterated local search
Simulation
Stochastic combinatorial optimization
Simheuristics
title_short SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
title_full SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
title_fullStr SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
title_full_unstemmed SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
title_sort SimILS: a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization
dc.creator.none.fl_str_mv Grasas, Àlex
Juan, Angel A.
Ramalhinho-Lourenço, Helena
author Grasas, Àlex
author_facet Grasas, Àlex
Juan, Angel A.
Ramalhinho-Lourenço, Helena
author_role author
author2 Juan, Angel A.
Ramalhinho-Lourenço, Helena
author2_role author
author
dc.subject.none.fl_str_mv Iterated local search
Simulation
Stochastic combinatorial optimization
Simheuristics
topic Iterated local search
Simulation
Stochastic combinatorial optimization
Simheuristics
description Iterated Local Search (ILS) is one of the most popular single-solution-based metaheuristics. ILS is recognized by many authors as a relatively simple yet efficient framework able to deal with complex combinatorial optimization problems (COPs). ILS-based algorithms have been successfully applied to provide near-optimal solutions to different COPs in logistics, transportation, production, etc. However, ILS is designed to solve COPs under deterministic scenarios. In some real-life applications where uncertainty is present, the deterministic assumption makes the model less accurate since it does not reflect the real stochastic nature of the system. This paper presents the SimILS framework that extends ILS by integrating simulation to be able to cope with Stochastic COPs in a natural way. The paper also describes several tested applications that illustrate the main concepts behind SimILS and give rise to a new brand of ILS-based algorithms.
publishDate 2014
dc.date.none.fl_str_mv 2014
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/44209
http://dx.doi.org/10.1057/jos.2014.25
url http://hdl.handle.net/10230/44209
http://dx.doi.org/10.1057/jos.2014.25
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Simulation. 2017 Oct 3;10(1):69-77
info:eu-repo/grantAgreement/ES/1PE/TRA2013-48180-C3-P
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403148187074560
score 15.812429

Registos relacionados