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Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem

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Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem

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dc.contributor.author Vallada Regalado, Eva es_ES
dc.contributor.author Villa Juliá, Mª Fulgencia es_ES
dc.contributor.author Fanjul-Peyro, Luis es_ES
dc.date.accessioned 2020-11-20T04:31:29Z
dc.date.available 2020-11-20T04:31:29Z
dc.date.issued 2019-11 es_ES
dc.identifier.issn 0305-0548 es_ES
dc.identifier.uri http://hdl.handle.net/10251/155401
dc.description.abstract [EN] A Scatter Search algorithm together with an enriched Scatter Search and an enriched Iterated Greedy for the unrelated parallel machine problem with one additional resource are proposed in this paper. The optimisation objective is to minimise the maximum completion of the jobs on the machines, that is, the makespan. All the proposed methods start from the best known heuristic for the same problem. Non feasible solutions are allowed in all the methods and a Repairing Mechanism is applied to obtain a feasible solution from a resource constraint point of view. All the proposed algorithms apply different local search procedures based on insertion, swap and restricted neighbourhoods. Computational experiments are carried out using an exhaustive benchmark of instances. After analysing the results, we can conclude that the enriched methods obtain superior results, outperforming the best known solutions for the same problem. 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. DPI201565895-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.ispartof Computers & Operations Research es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Unrelated parallel machine es_ES
dc.subject Scheduling es_ES
dc.subject Additional scarce resource es_ES
dc.subject Metaheuristics es_ES
dc.subject Makespan es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cor.2019.07.016 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/731932/EU/Transforming Transport/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2014-53665-P/ES/OPTIMIZACION DE PROCESOS EN TERMINALES MARITIMAS DE CONTENEDORES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2015-65895-R/ES/OPTIMIZATION OF SCHEDULING PROBLEMS IN CONTAINER YARDS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094940-B-I00/ES/OPTIMIZACION DE OPERACIONES EN TERMINALES PORTUARIAS/ es_ES
dc.rights.accessRights Embargado es_ES
dc.date.embargoEndDate 2022-11-30 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 Vallada Regalado, E.; Villa Juliá, MF.; Fanjul-Peyro, L. (2019). Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem. Computers & Operations Research. 111:415-424. https://doi.org/10.1016/j.cor.2019.07.016 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cor.2019.07.016 es_ES
dc.description.upvformatpinicio 415 es_ES
dc.description.upvformatpfin 424 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 111 es_ES
dc.relation.pasarela S\408640 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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