Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop

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@Article{Baykasoglu:2010:S,
  title =        "Genetic Programming Based Data Mining Approach to
                 Dispatching Rule Selection in a Simulated Job Shop",
  author =       "Adil Baykasoglu and Mustafa Gocken and Lale Ozbakir",
  journal =      "Simulation",
  year =         "2010",
  number =       "12",
  volume =       "86",
  pages =        "715--728",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 dispatching rules",
  DOI =          "doi:10.1177/0037549709346561",
  size =         "14 pages",
  bibdate =      "2011-02-04",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/simulation/simulation86.html#BaykasogluGO10",
  abstract =     "In this paper, a genetic programming based data mining
                 approach is proposed to select dispatching rules which
                 will result in competitive shop performance under a
                 given set of shop parameters (e.g. interarrival times,
                 pre-shop pool length). The main purpose is to select
                 the most appropriate conventional dispatching rule set
                 according to the current shop parameters. In order to
                 achieve this, full factorial experiments are carried
                 out to determine the effect of input parameters on
                 predetermined performance measures. Afterwards, a
                 genetic programming based data mining tool that is
                 known as MEPAR-miner (multi-expression programming for
                 classification rule mining) is employed to extract
                 knowledge on the selection of best possible
                 conventional dispatching rule set according to the
                 current shop status. The obtained results have shown
                 that the selected dispatching rules are appropriate
                 ones according to the current shop parameters. All of
                 the results are illustrated via numerical examples and
                 experiments on simulated data.",
}

Genetic Programming entries for Adil Baykasoglu Mustafa Gocken Lale Ozbakir

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