Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop
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- @Article{Baykasoglu:2010:S,
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title = "Genetic Programming Based Data Mining Approach to
Dispatching Rule Selection in a Simulated Job Shop",
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author = "Adil Baykasoglu and Mustafa Gocken and Lale Ozbakir",
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journal = "Simulation",
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year = "2010",
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number = "12",
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volume = "86",
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pages = "715--728",
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keywords = "genetic algorithms, genetic programming, data mining,
dispatching rules",
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DOI = "doi:10.1177/0037549709346561",
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size = "14 pages",
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bibdate = "2011-02-04",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/simulation/simulation86.html#BaykasogluGO10",
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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
Citations