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Optimizing Dispatching Rules for Stochastic Job Shop Scheduling

2019; Springer Nature; Linguagem: Inglês

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

2194-5357

Cristiane Ferreira, Gonçalo Figueira, Pedro Amorim,

Assembly Line Balancing Optimization

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.