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A Genetic Algorithm for Energy-Efficiency in Job-Shop Scheduling

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A Genetic Algorithm for Energy-Efficiency in Job-Shop Scheduling

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Nombre: Miguel A. Salido; ...
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dc.contributor.author Salido, Miguel A. es_ES
dc.contributor.author Escamilla Fuster, Joan es_ES
dc.contributor.author Giret Boggino, Adriana Susana es_ES
dc.contributor.author Barber, Federico es_ES
dc.date.accessioned 2016-09-08T07:46:42Z
dc.date.available 2016-09-08T07:46:42Z
dc.date.issued 2016-07
dc.identifier.issn 0268-3768
dc.identifier.uri http://hdl.handle.net/10251/69061
dc.description.abstract Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energyefficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical jobshop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality. es_ES
dc.description.sponsorship This research has been supported by the Spanish Government under research project MINECO TIN2013-46511-C2-1 and the CASES project supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under the grant agreement No 294931. We would like to thanks Philippe Laborie (IBM ILOG CPLEX) for validating the CP Optimizer model for this problem. We also appreciate the significant efforts made by all the reviewers to improve this paper. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof International Journal of Advanced Manufacturing Technology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Job-shop scheduling problems es_ES
dc.subject Metaheuristic es_ES
dc.subject Energy-efficiency es_ES
dc.subject Robustness es_ES
dc.subject Makespan es_ES
dc.subject Artificial intelligence es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Genetic Algorithm for Energy-Efficiency in Job-Shop Scheduling es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00170-015-7987-0
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2013-46511-C2-1-P/ES/TECNICAS INTELIGENTES PARA LA OBTENCION DE SOLUCIONES ROBUSTAS Y EFICIENTES ENERGETICAMENTE EN SCHEDULING: APLICACION AL TRANSPORTE::UPV/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/294931/EU/Customised Advisory Services for Energy-efficient Manufacturing Systems/
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Salido, MA.; Escamilla Fuster, J.; Giret Boggino, AS.; Barber, F. (2016). A Genetic Algorithm for Energy-Efficiency in Job-Shop Scheduling. International Journal of Advanced Manufacturing Technology. 85(5-8):1303-1314. https://doi.org/10.1007/s00170-015-7987-0 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s00170-015-7987-0 es_ES
dc.description.upvformatpinicio 1303 es_ES
dc.description.upvformatpfin 1314 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 85 es_ES
dc.description.issue 5-8 es_ES
dc.relation.senia 294862 es_ES
dc.identifier.eissn 1433-3015
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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