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Campo Dublin Core | Valor | Idioma |
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dc.contributor.author | Rosa, Bruno Ferreira | - |
dc.contributor.author | Souza, Marcone Jamilson Freitas | - |
dc.contributor.author | Souza, Sergio Ricardo de | - |
dc.date.accessioned | 2019-05-17T14:55:57Z | - |
dc.date.available | 2019-05-17T14:55:57Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | ROSA, B. F.; SOUZA, M. J. F.; SOUZA, S. R. de. Algorithms based on VNS for solving the Single Machine Scheduling Problem with Earliness and Tardiness Penalties. Electronic Notes in Discrete Mathematics, v. 66, p. 47-54, abr. 2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1571065318300532#!>. Acesso em: 19 mar. 2019. | pt_BR |
dc.identifier.issn | 1571-0653 | - |
dc.identifier.uri | http://www.repositorio.ufop.br/handle/123456789/11343 | - |
dc.description.abstract | This work implements and compares four algorithms based on Variable Neighborhood Search (VNS), named RVNS, GVNSf, GVNSr and GVNSrf, for solving the Single Machine Scheduling Problem with Earliness and Tardiness Penalties (SM-SPETP). Computational experiments showed that the algorithm GVNSf obtained better-quality solutions compared with the other algorithms, including an algorithm found in the literature. The algorithms GVNSr and GVNSrf obtained solutions close to the GVNSf, and outperformed the algorithm of the literature, both with respect to the quality of the solutions and the computational times. | pt_BR |
dc.language.iso | en_US | pt_BR |
dc.rights | restrito | pt_BR |
dc.subject | Sequence-Dependent Setup Times | pt_BR |
dc.title | Algorithms based on VNS for solving the Single Machine Scheduling Problem with Earliness and Tardiness Penalties. | pt_BR |
dc.type | Artigo publicado em periodico | pt_BR |
dc.identifier.uri2 | https://www.sciencedirect.com/science/article/pii/S1571065318300532 | pt_BR |
dc.identifier.doi | https://doi.org/10.1016/j.endm.2018.03.007 | pt_BR |
Aparece nas coleções: | DECOM - Artigos publicados em periódicos |
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ARTIGO_AlgorithmsBasedVNS.pdf Restricted Access | 211,36 kB | Adobe PDF | Visualizar/Abrir |
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