Optimizing Dispatching Rules for Stochastic Job Shop Scheduling

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Ferreira, C (Autor) Outra; figueira, g (Autor) FEUP; Pedro Amorim (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the 18th International Conference on Hybrid Intelligent Systems (HIS 2018)

Páginas: 321-330

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00Q-GFR

DOI: 10.1007/978-3-030-14347-3_31

Abstract (EN): Manufacturing environments commonly present uncertainties and unexpected schedule disruptions. The literature has shown that in these environments simple and fast dynamic dispatching rules are efficient sequencing methods. However, most of the works in the automated designing of these rules have considered deterministic processing times. This work aims to design dispatching rules for problem settings similar to the ones found in real environments such as uncertain processing times and sequence-dependent setup times. We use Genetic Programming to generate efficient rules for stochastic job shops with setup times. We show that the generated rules outperform benchmark dispatching rules, specially in settings with high setup time levels. © 2020, Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica