Abstract
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.
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Acknowledgments
This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project SAICTPAC/0034/2015- POCI-01-0145-FEDER-016418.
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Ferreira, C., Figueira, G., Amorim, P. (2020). Optimizing Dispatching Rules for Stochastic Job Shop Scheduling. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_31
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DOI: https://doi.org/10.1007/978-3-030-14347-3_31
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