Analysis of stochastic local search methods for the unrelatedparallel machine scheduling problem.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorSantos, Haroldo Gambini-
Autor(es): dc.creatorToffolo, Túlio Ângelo Machado-
Autor(es): dc.creatorSilva, Cristiano Luís Turbino de França e-
Autor(es): dc.creatorBerghe, Greet Vanden-
Data de aceite: dc.date.accessioned2025-08-21T15:05:14Z-
Data de disponibilização: dc.date.available2025-08-21T15:05:14Z-
Data de envio: dc.date.issued2017-02-01-
Data de envio: dc.date.issued2017-02-01-
Data de envio: dc.date.issued2016-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/7171-
Fonte completa do material: dc.identifierhttp://onlinelibrary.wiley.com/doi/10.1111/itor.12316/epdf-
Fonte completa do material: dc.identifierhttps://doi.org/10.1111/itor.12316-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1002182-
Descrição: dc.descriptionThis work addresses the unrelated parallel machine scheduling problem with sequence-dependent setup times,in which a set of jobs must be scheduled for execution by one of the several available machines. Each jobhas a machine-dependent processing time. Furthermore, given multiple jobs, there are additional setup times,which vary based on the sequence and machine employed. The objective is to minimiz e the schedule’s com-pletion time (makespan). The problem is NP-hard and of significant practical relevance. The present paperinvestigates the performance of four different stochastic local search (SLS) methods designed for solvingthe particular scheduling problem: simulated annealing, iterated local search, late acceptance hill-climbing,and step counting hill-climbing. The analysis focuses on design questions, tuning effort, and optimizationperformance. Simple neighborhood structures are considered. All proposed SLS methods performed signifi-cantly better than two state-of-the-art hybrid heuristics, especially for larger instances. Updated best-knownsolutions were generated for 901 of the 1000 large benchmark instances considered, demonstrating that par-ticular SLS methods are simple yet powerful alternatives to current approaches for addressing the problem.Implementations of the contributed algorithms have been made available to the research community.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectHeuristics-
Palavras-chave: dc.subjectMetaheuristics-
Título: dc.titleAnalysis of stochastic local search methods for the unrelatedparallel machine scheduling problem.-
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