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

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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

Citations