Biased random-key genetic algorithm for the job sequencing and tool switching problem with non-identical parallel machines.

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Autor(es): dc.creatorSoares, Leonardo Cabral da Rocha-
Autor(es): dc.creatorCarvalho, Marco Antonio Moreira de-
Data de aceite: dc.date.accessioned2025-08-21T15:34:44Z-
Data de disponibilização: dc.date.available2025-08-21T15:34:44Z-
Data de envio: dc.date.issued2025-01-16-
Data de envio: dc.date.issued2025-01-16-
Data de envio: dc.date.issued2023-
Fonte completa do material: dc.identifierhttps://www.repositorio.ufop.br/handle/123456789/19574-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0305054823003738-
Fonte completa do material: dc.identifierhttps://doi.org/10.1016/j.cor.2023.106509-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1018798-
Descrição: dc.descriptionWe address the job sequencing and tool switching problem associated with non-identical parallel machines – a variation of the well-known sequencing and switching problem (SSP) better adapted to reflect the challenges in modern production environments. The NP-hard problem is approached by considering two isolated objective functions: the minimization of the makespan and the minimization of the total flow time. We present two versions of a parallel biased random-key genetic algorithm hybridized with tailored local search procedures that are organized using variable neighborhood descent. The proposed methods are compared with state-of-theart methods by considering 640 benchmark instances from literature. For both objective functions considered, the proposed methods consistently outperform the compared methods. All known optimal values for both objectives are achieved, and a substantial gap is reported for all instance groups when compared with the best previously published solution values.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectCombinatorial optimization-
Palavras-chave: dc.subjectBiased random-key genetic algorithm-
Palavras-chave: dc.subjectJob sequencing and tool switching problem-
Palavras-chave: dc.subjectWith non-identical parallel machines-
Palavras-chave: dc.subjectFlexible manufacturing system-
Título: dc.titleBiased random-key genetic algorithm for the job sequencing and tool switching problem with non-identical parallel machines.-
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