A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs

This paper analyzes a realistic variant of the Permutation Flow-Shop Problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature revi...

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Detalles Bibliográficos
Autores: Ferrer, Alberto, Guimarans, Daniel, Ramalhinho-Lourenço, Helena, Juan, Angel A.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2016
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/47668
Acceso en línea:http://hdl.handle.net/10230/47668
http://dx.doi.org/10.1016/j.eswa.2015.09.011
Access Level:acceso abierto
Palabra clave:Heuristic algorithms
Biased randomization
Iterated Local Search
Scheduling
Flow-shop
Non-smooth objective functions
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spelling A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costsFerrer, AlbertoGuimarans, DanielRamalhinho-Lourenço, HelenaJuan, Angel A.Heuristic algorithmsBiased randomizationIterated Local SearchSchedulingFlow-shopNon-smooth objective functionsThis paper analyzes a realistic variant of the Permutation Flow-Shop Problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a Biased-Randomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the well-known NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of non-smooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failure-risk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other state-of-the-art metaheuristic approaches, which have been conveniently adapted to consider failure-risk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar non-smooth objective functions.This research has been partially supported by the Spanish Ministry of Economy and Competitiveness, projects MTM2011-29064-C03-02, MTM2014-59179-C2-01 & TRA2013-48180-C3-P, and FEDER. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.Elsevier202120212016info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/47668http://dx.doi.org/10.1016/j.eswa.2015.09.011reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésExpert Systems with Applications. 2016 Feb;44:177–86info:eu-repo/grantAgreement/ES/3PN/MTM2011-29064-C03-02info:eu-repo/grantAgreement/ES/1PE/MTM2014-59179-C2-01info:eu-repo/grantAgreement/ES/1PE/TRA2013-48180-C3-P© Elsevier http://dx.doi.org/10.1016/j.eswa.2015.09.011info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/476682026-06-12T07:21:37Z
dc.title.none.fl_str_mv A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
title A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
spellingShingle A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
Ferrer, Alberto
Heuristic algorithms
Biased randomization
Iterated Local Search
Scheduling
Flow-shop
Non-smooth objective functions
title_short A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
title_full A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
title_fullStr A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
title_full_unstemmed A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
title_sort A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
dc.creator.none.fl_str_mv Ferrer, Alberto
Guimarans, Daniel
Ramalhinho-Lourenço, Helena
Juan, Angel A.
author Ferrer, Alberto
author_facet Ferrer, Alberto
Guimarans, Daniel
Ramalhinho-Lourenço, Helena
Juan, Angel A.
author_role author
author2 Guimarans, Daniel
Ramalhinho-Lourenço, Helena
Juan, Angel A.
author2_role author
author
author
dc.subject.none.fl_str_mv Heuristic algorithms
Biased randomization
Iterated Local Search
Scheduling
Flow-shop
Non-smooth objective functions
topic Heuristic algorithms
Biased randomization
Iterated Local Search
Scheduling
Flow-shop
Non-smooth objective functions
description This paper analyzes a realistic variant of the Permutation Flow-Shop Problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a Biased-Randomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the well-known NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of non-smooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failure-risk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other state-of-the-art metaheuristic approaches, which have been conveniently adapted to consider failure-risk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar non-smooth objective functions.
publishDate 2016
dc.date.none.fl_str_mv 2016
2021
2021
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/10230/47668
http://dx.doi.org/10.1016/j.eswa.2015.09.011
url http://hdl.handle.net/10230/47668
http://dx.doi.org/10.1016/j.eswa.2015.09.011
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications. 2016 Feb;44:177–86
info:eu-repo/grantAgreement/ES/3PN/MTM2011-29064-C03-02
info:eu-repo/grantAgreement/ES/1PE/MTM2014-59179-C2-01
info:eu-repo/grantAgreement/ES/1PE/TRA2013-48180-C3-P
dc.rights.none.fl_str_mv © Elsevier http://dx.doi.org/10.1016/j.eswa.2015.09.011
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Elsevier http://dx.doi.org/10.1016/j.eswa.2015.09.011
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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