A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem
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- @Article{Nguyen:2013:ieeeTEC,
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author = "Su Nguyen and Mengjie Zhang and Mark Johnston and
Kay Chen Tan",
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title = "A Computational Study of Representations in Genetic
Programming to Evolve Dispatching Rules for the Job
Shop Scheduling Problem",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2013",
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volume = "17",
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number = "5",
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pages = "621--639",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Grammar, Job
shop scheduling, Processor scheduling, Schedules,
dispatching rule, hyper-heuristic, job shop
scheduling,",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2012.2227326",
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size = "19 pages",
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abstract = "Designing effective dispatching rules is an important
factor for many manufacturing systems. However, this
time consuming process has been performed manually for
a very long time. Recently, some machine learning
approaches have been proposed to support this task. In
this paper, we investigate the use of genetic
programming for automatically discovering new
dispatching rules for the single objective job shop
scheduling problem (JSP). Different representations of
the dispatching rules in the literature and newly
proposed in this work are compared and analysed.
Experimental results show that the representation which
integrates system and machine attributes can improve
the quality of the evolved rules. Analysis of the
evolved rules also provides useful knowledge about how
these rules can effectively solve JSP.",
-
notes = "also known as \cite{6353198}",
- }
Genetic Programming entries for
Su Nguyen
Mengjie Zhang
Mark Johnston
Kay Chen Tan
Citations