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
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/155401
Title:
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Enriched metaheuristics for the resource constrained unrelated parallel machine scheduling problem
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Author:
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Vallada Regalado, Eva
Villa Juliá, Mª Fulgencia
Fanjul-Peyro, Luis
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UPV Unit:
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Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
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Issued date:
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Abstract:
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[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 ...[+]
[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.
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Subjects:
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Unrelated parallel machine
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Scheduling
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Additional scarce resource
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Metaheuristics
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Makespan
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Copyrigths:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Source:
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Computers & Operations Research. (issn:
0305-0548
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DOI:
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10.1016/j.cor.2019.07.016
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Publisher:
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Elsevier
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Publisher version:
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https://doi.org/10.1016/j.cor.2019.07.016
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Project ID:
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info:eu-repo/grantAgreement/EC/H2020/731932/EU/Transforming Transport/
info:eu-repo/grantAgreement/MINECO//DPI2014-53665-P/ES/OPTIMIZACION DE PROCESOS EN TERMINALES MARITIMAS DE CONTENEDORES/
info:eu-repo/grantAgreement/MINECO//DPI2015-65895-R/ES/OPTIMIZATION OF SCHEDULING PROBLEMS IN CONTAINER YARDS/
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/
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Thanks:
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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 ...[+]
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.
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Type:
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Artículo
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