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Campo Dublin Core | Valor | Idioma |
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dc.contributor.author | Santos, Haroldo Gambini | - |
dc.contributor.author | Toffolo, Túlio Ângelo Machado | - |
dc.contributor.author | Silva, Cristiano Luís Turbino de França e | - |
dc.contributor.author | Berghe, Greet Vanden | - |
dc.date.accessioned | 2017-02-01T12:51:01Z | - |
dc.date.available | 2017-02-01T12:51:01Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | SANTOS, H. G. et al. Analysis of stochastic local search methods for the unrelatedparallel machine scheduling problem. International Transactions in Operational Research, v. 26, p. 707-724, 2016. Disponível em: <http://onlinelibrary.wiley.com/doi/10.1111/itor.12316/epdf>. Acesso em: 20 jan. 2017. | pt_BR |
dc.identifier.issn | 1475-3995 | - |
dc.identifier.uri | http://www.repositorio.ufop.br/handle/123456789/7171 | - |
dc.description.abstract | This 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. | pt_BR |
dc.language.iso | en_US | pt_BR |
dc.rights | restrito | pt_BR |
dc.subject | Heuristics | pt_BR |
dc.subject | Metaheuristics | pt_BR |
dc.title | Analysis of stochastic local search methods for the unrelatedparallel machine scheduling problem. | pt_BR |
dc.type | Artigo publicado em periodico | pt_BR |
dc.identifier.uri2 | http://onlinelibrary.wiley.com/doi/10.1111/itor.12316/epdf | pt_BR |
dc.identifier.doi | https://doi.org/10.1111/itor.12316 | - |
Aparece nas coleções: | DECOM - Artigos publicados em periódicos |
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ARTIGO_AnalysisStochasticLocal.pdf Restricted Access | 963,61 kB | Adobe PDF | Visualizar/Abrir |
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