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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