Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems

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@Article{Tay2008453,
  title =        "Evolving dispatching rules using genetic programming
                 for solving multi-objective flexible job-shop
                 problems",
  author =       "Joc Cing Tay and Nhu Binh Ho",
  journal =      "Computers \& Industrial Engineering",
  volume =       "54",
  number =       "3",
  pages =        "453--473",
  year =         "2008",
  ISSN =         "0360-8352",
  DOI =          "doi:10.1016/j.cie.2007.08.008",
  URL =          "http://www.sciencedirect.com/science/article/B6V27-4PKXBN1-1/2/5821882f2443c0fb1fff7c462c34e793",
  keywords =     "genetic algorithms, genetic programming, Flexible job
                 shop, Production scheduling, Dispatching rules",
  abstract =     "We solve the multi-objective flexible job-shop
                 problems by using dispatching rules discovered through
                 genetic programming. While Simple Priority Rules have
                 been widely applied in practice, their efficacy remains
                 poor due to lack of a global view. Composite
                 dispatching rules have been shown to be more effective
                 as they are constructed through human experience. In
                 this paper, we evaluate and employ suitable parameter
                 and operator spaces for evolving composite dispatching
                 rules using genetic programming, with an aim towards
                 greater scalability and flexibility. Experimental
                 results show that composite dispatching rules generated
                 by our genetic programming framework outperforms the
                 single dispatching rules and composite dispatching
                 rules selected from literature over five large
                 validation sets with respect to minimum makespan, mean
                 tardiness, and mean flow time objectives. Further
                 results on sensitivity to changes (in coefficient
                 values and terminals among the evolved rules) indicate
                 that their designs are robust.",
}

Genetic Programming entries for Joc Cing Tay Nhu Binh Ho

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