Learning iterative dispatching rules for job shop scheduling with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Nguyen:2013:ijamt,
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "Learning iterative dispatching rules for job shop
                 scheduling with genetic programming",
  journal =      "The International Journal of Advanced Manufacturing
                 Technology",
  year =         "2013",
  volume =       "67",
  number =       "1-4",
  pages =        "85--100",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Job shop,
                 Dispatching rule, Local search",
  ISSN =         "0268-3768",
  DOI =          "doi:10.1007/s00170-013-4756-9",
  abstract =     "This study proposes a new type of dispatching rule for
                 job shop scheduling problems. The novelty of these
                 dispatching rules is that they can iteratively improve
                 the schedules by using the information from completed
                 schedules. While the quality of the schedule can be
                 improved, the proposed iterative dispatching rules
                 (IDRs) still maintain the easiness of implementation
                 and low computational effort of the traditional
                 dispatching rules. This feature makes them more
                 attractive for large-scale manufacturing systems. A
                 genetic programming (GP) method is developed in this
                 paper to evolve IDRs for job shop scheduling problems.
                 The results show that the proposed GP method is
                 significantly better than the simple GP method for
                 evolving composite dispatching rules. The evolved IDRs
                 also show their superiority to the benchmark
                 dispatching rules when tested on different problem
                 instances with makespan and total weighted tardiness as
                 the objectives. Different aspects of IDRs are also
                 investigated and the insights from these analyses are
                 used to enhance the performance of IDRs.",
  language =     "English",
}

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston Kay Chen Tan

Citations