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Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

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Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

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dc.contributor.author Villa Juliá, Mª Fulgencia es_ES
dc.contributor.author Vallada Regalado, Eva es_ES
dc.contributor.author Fanjul Peyró, Luis es_ES
dc.date.accessioned 2019-06-28T20:04:51Z
dc.date.available 2019-06-28T20:04:51Z
dc.date.issued 2018 es_ES
dc.identifier.issn 0957-4174 es_ES
dc.identifier.uri http://hdl.handle.net/10251/122887
dc.description.abstract [EN] In this paper, we study the unrelated parallel machine scheduling problem with one scarce additional resource to minimise the maximum completion time of the jobs or makespan. Several heuristics are proposed following two strategies: the first one is based on the consideration of the resource constraint during the whole solution construction process. The second one starts from several assignment rules without considering the resource constraint, and repairs the non feasible assignments in order to obtain a feasible solution. Several computation experiments are carried out over an extensive benchmark. A comparative evaluation against previously proposed mathematical models and matheuristics (combination of mathematical models and heuristics) is carried out. From the results, we can conclude that our methods outperform the existing ones, and the second strategy performs better, especially for large instances. (C) 2017 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship The authors are supported by the Spanish Ministry of Economy and Competitiveness, under the projects "SCHEYARD - Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) and "OPTEMAC - Optimizacion de Procesos en Terminales Maritimas de Contenedores" (No. DPI2014-53665-P), all of them partially financed with FEDER funds. The authors are also partially supported by the EU Horizon 2020 research and innovation programme under grant agreement no. 731932 "Transforming Transport: Big Data Value in Mobility and Logistics". Interested readers can download contents from http://soa.iti.es, like the instances used and a software for generating further instances. Source codes are available upon justified request from the authors. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation MINECO/DPI2015-65895-R es_ES
dc.relation MINECO/DPI2015-53665-P es_ES
dc.relation.ispartof Expert Systems with Applications es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Parallel machine problem es_ES
dc.subject Scheduling es_ES
dc.subject Additional resources es_ES
dc.subject Heuristics es_ES
dc.subject Makespan es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.eswa.2017.09.054 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/731932/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Villa Juliá, MF.; Vallada Regalado, E.; Fanjul Peyró, L. (2018). Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource. Expert Systems with Applications. 93:28-38. https://doi.org/10.1016/j.eswa.2017.09.054 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1016/j.eswa.2017.09.054 es_ES
dc.description.upvformatpinicio 28 es_ES
dc.description.upvformatpfin 38 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 93 es_ES
dc.relation.pasarela S\348425 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES


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