Large-scale simulation-based optimization of semiconductor dispatching rules

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Torsten Hildebrandt and Debkalpa Goswami and 
                 Michael Freitag",
  booktitle =    "Winter Simulation Conference (WSC 2014)",
  title =        "Large-scale simulation-based optimization of
                 semiconductor dispatching rules",
  year =         "2014",
  month =        dec,
  pages =        "2580--2590",
  keywords =     "genetic algorithms, genetic programming, MIMAC FAB6",
  DOI =          "doi:10.1109/WSC.2014.7020102",
  size =         "11 pages",
  abstract =     "Developing dispatching rules for complex production
                 systems such as semiconductor manufacturing is an
                 involved task usually performed manually. In a tedious
                 trial-and-error process, a human expert attempts to
                 improve existing rules, which are evaluated using
                 discrete-event simulation. A significant improvement in
                 this task can be achieved by coupling a discrete-event
                 simulator with heuristic optimisation algorithms. In
                 this paper we show that this approach is feasible for
                 large manufacturing scenarios as well, and it is also
                 useful to quantify the value of information for the
                 scheduling process. Using the objective of minimising
                 the mean cycle time of lots, we show that rules created
                 automatically using Genetic Programming (GP) can
                 clearly outperform standard rules. We compare their
                 performance to manually developed rules from the
  notes =        "BIBA-Bremer Inst. fur Produktion und Logistik GmbH at
                 the ArcelorMittal Bremen, Univ. Bremen, Bremen,

                 Also known as \cite{7020102}",

Genetic Programming entries for Torsten Hildebrandt Debkalpa Goswami Michael Freitag