A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Nguyen:2013:ieeeTEC,
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "A Computational Study of Representations in Genetic
                 Programming to Evolve Dispatching Rules for the Job
                 Shop Scheduling Problem",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2013",
  volume =       "17",
  number =       "5",
  pages =        "621--639",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Grammar, Job
                 shop scheduling, Processor scheduling, Schedules,
                 dispatching rule, hyper-heuristic, job shop
                 scheduling,",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2012.2227326",
  size =         "19 pages",
  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