A demonstration of machine learning for explicit functions for cycle time prediction using MES data

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@InProceedings{Can:2016:WSC,

• author = "Birkan Can and Cathal Heavey",
• booktitle = "2016 Winter Simulation Conference (WSC)",
• title = "A demonstration of machine learning for explicit functions for cycle time prediction using MES data",
• year = "2016",
• pages = "2500--2511",
• abstract = "Cycle time prediction represents a challenging problem in complex manufacturing scenarios. This paper demonstrates an approach that uses genetic programming (GP) and effective process time (EPT) to predict cycle time using a discrete event simulation model of a production line, an approach that could be used in complex manufacturing systems, such as a semiconductor fab. These predictive models could be used to support control and planning of manufacturing systems. GP results in a more explicit function for cycle time prediction. The results of the proposed approach show a difference between 1-6percent on the demonstrated production line.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/WSC.2016.7822289",
• month = dec,
• notes = "Also known as \cite{7822289}",

}

Genetic Programming entries for Birkan Can Cathal Heavey

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