Heuristics for multi-objective no-wait flow shops with sequence-dependent setup times
Productive systems often involve several objectives and constraints that need to be considered by the scheduler. Under these circumstances, solving scheduling problems with multiple criteria tends to be the most appropriate approach. In this context, the no-wait flow shop problem with sequence-depen...
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| Tipo de documento: | dissertação |
| Estado: | Versão publicada |
| Data de publicação: | 2021 |
| País: | Brasil |
| Recursos: | Universidade de São Paulo (USP) |
| Repositório: | Biblioteca Digital de Teses e Dissertações da USP |
| Idioma: | inglês |
| OAI Identifier: | oai:teses.usp.br:tde-07122021-164135 |
| Acesso em linha: | https://www.teses.usp.br/teses/disponiveis/18/18156/tde-07122021-164135/ |
| Access Level: | Acceso aberto |
| Palavra-chave: | Flow shop Makespan No-wait Sequence-dependent setup times Total completion time Total tardiness |
| Resumo: | Productive systems often involve several objectives and constraints that need to be considered by the scheduler. Under these circumstances, solving scheduling problems with multiple criteria tends to be the most appropriate approach. In this context, the no-wait flow shop problem with sequence-dependent setup times is addressed. The performance measures makespan, total completion time and total tardiness are approached in pairs to form functions ε(M1|M2), in which the objective is to minimize M1 subject to an upper bound on M2. Since this problem is known to be NP-hard, using exact methods for large instances can be impractical. As an alternative, heuristic methods have been developed to speed up the process of finding satisfactory solutions. In this Thesis, state-of-the-art methods for similar problems found in the literature are selected in order to explore opportunities for improvement. Focusing on simplicity of implementation and efficiency of execution, different heuristic methods are proposed. Extensive experiments are performed to evaluate performance. The results show that the proposed heuristics outperform the existing methods in solution quality and computational efficiency. |
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