Evolving Machine-Specific Dispatching Rules for a Two-Machine Job Shop using Genetic Programming

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

@InProceedings{Hunt:2014:CEC,
  title =        "Evolving Machine-Specific Dispatching Rules for a
                 Two-Machine Job Shop using Genetic Programming",
  author =       "Rachel Hunt and Mark Johnston and Mengjie Zhang",
  pages =        "618--625",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computation for Planning and Scheduling",
  DOI =          "doi:10.1109/CEC.2014.6900655",
  abstract =     "Job Shop Scheduling (JSS) involves determining a
                 schedule for processing jobs on machines to optimise
                 some measure of delivery speed or customer
                 satisfaction. We investigate a genetic programming
                 based hyper-heuristic (GPHH) approach to evolving
                 dispatching rules for a two-machine job shop in both
                 static and dynamic environments. In the static case the
                 proposed GPHH method can represent and discover optimal
                 dispatching rules. In the dynamic case we investigate
                 two representations (using a single rule at both
                 machines and evolving a specialised rule for each
                 machine) and the effect of changing the training
                 problem instances throughout evolution. Results show
                 that relative performance of these methods is dependent
                 on the testing instances.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Rachel Hunt Mark Johnston Mengjie Zhang

Citations