Genetic Programming based Hyper-heuristics for Dynamic Job Shop Scheduling: Cooperative Coevolutionary Approaches

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

@InProceedings{Park:2016:EuroGP,
  author =       "John Park and Yi Mei and Su Nguyen and Gang Chen2 and 
                 Mengjie Zhang",
  title =        "Genetic Programming based Hyper-heuristics for Dynamic
                 Job Shop Scheduling: Cooperative Coevolutionary
                 Approaches",
  booktitle =    "EuroGP 2016: Proceedings of the 19th European
                 Conference on Genetic Programming",
  year =         "2016",
  month =        "30 " # mar # "--1 " # apr,
  editor =       "Malcolm I. Heywood and James McDermott and 
                 Mauro Castelli and Ernesto Costa and Kevin Sim",
  series =       "LNCS",
  volume =       "9594",
  publisher =    "Springer Verlag",
  address =      "Porto, Portugal",
  pages =        "115--132",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-30668-1",
  DOI =          "doi:10.1007/978-3-319-30668-1_8",
  abstract =     "Job shop scheduling (JSS) problems are optimisation
                 problems that have been studied extensively due to
                 their computational complexity and application in
                 manufacturing systems. This paper focuses on a dynamic
                 JSS problem to minimise the total weighted tardiness.
                 In dynamic JSS, attributes of a job are only revealed
                 after it arrives at the shop floor. Dispatching rule
                 heuristics are prominent approaches to dynamic JSS
                 problems, and Genetic Programming based Hyper-heuristic
                 (GP-HH) approaches have been proposed to automatically
                 generate effective dispatching rules for dynamic JSS
                 problems. Research on static JSS problems shows that
                 high quality ensembles of dispatching rules can be
                 evolved by a GP-HH that uses cooperative coevolution.
                 Therefore, we compare two coevolutionary GP approaches
                 to evolve ensembles of dispatching rules for dynamic
                 JSS problems. First, we adapt the Multilevel Genetic
                 Programming (MLGP) approach, which has never been
                 applied to JSS problems. Second, we extend an existing
                 approach for a static JSS problem, called Ensemble
                 Genetic Programming for Job Shop Scheduling (EGP-JSS),
                 by adding less-myopic terminals that take job and
                 machine attributes outside of the scope of the
                 attributes commonly used in the literature. The results
                 show that MLGP for JSS evolves ensembles that are
                 significantly better than single less-myopic rules
                 evolved using GP with only little difference in
                 computation time. In addition, the rules evolved using
                 EGP-JSS perform better than the MLGP-JSS rules, but
                 MLGP-JSS evolves rules significantly faster than
                 EGP-JSS.",
  notes =        "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
                 conjunction with EvoCOP2016, EvoMusArt2016 and
                 EvoApplications2016",
}

Genetic Programming entries for John Park Yi Mei Su Nguyen Gang Chen2 Mengjie Zhang

Citations