Evolving control rules for a dual-constrained job scheduling scenario

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

  author =       "Juergen Branke and Matthew J. Groves and 
                 Torsten Hildebrandt",
  booktitle =    "2016 Winter Simulation Conference (WSC)",
  title =        "Evolving control rules for a dual-constrained job
                 scheduling scenario",
  year =         "2016",
  pages =        "2568--2579",
  abstract =     "Dispatching rules are often used for scheduling in
                 semiconductor manufacturing due to the complexity and
                 stochasticity of the problem. In the past,
                 simulation-based Genetic Programming has been shown to
                 be a powerful tool to automate the time-consuming and
                 expensive process of designing such rules. However, the
                 scheduling problems considered were usually only
                 constrained by the capacity of the machines. In this
                 paper, we extend this idea to dual-constrained flow
                 shop scheduling, with machines and operators for
                 loading and unloading to be scheduled simultaneously.
                 We show empirically on a small test problem with
                 parallel workstations, re-entrant flows and dynamic
                 stochastic job arrival that the approach is able to
                 generate dispatching rules that perform significantly
                 better than benchmark rules from the literature.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/WSC.2016.7822295",
  month =        dec,
  notes =        "Also known as \cite{7822295}",

Genetic Programming entries for Jurgen Branke Matthew J Groves Torsten Hildebrandt