Solving Facility Layout Problems Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{garces-perez:1996:sflp,
  author =       "Jaime Garces-Perez and Dale A. Schoenefeld and 
                 Roger L. Wainwright",
  title =        "Solving Facility Layout Problems Using Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and 
                 David B. Fogel and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "182--190",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://euler.utulsa.edu/~rogerw/papers/Garces-Perez-flp.pdf",
  size =         "9 pages",
  abstract =     "This research applies techniques and tools from
                 Genetic Programming GP to the facility layout problem
                 The facility layout problem FLP is an NP-complete
                 combinatorial optimisation problem that has
                 applications to efficient facility design for
                 manufacturing and service industries. A facility layout
                 is represented as a collection of rectangular blocks
                 using a slicing tree structure (STS) We use a multiple
                 purpose genetic programming kernel to generate slicing
                 trees that are converted into candidate solutions for
                 an FLP The utility of our techniques is established
                 using eight previously published benchmark problems Our
                 genetic programming techniques that evolve STSs are
                 more natural and more flexible than all of the
                 previously published genetic algorithm and simulated
                 annealing techniques Previous genetic algorithm
                 techniques use a twophase optimisation strategy The
                 first phase uses clustering techniques to determine a
                 near optimal fixed tree structure that is represented
                 as a chromosome in a genetic algo rithm Within the
                 constraints implied by the fixed tree structure genetic
                 algorithm techniques are applied during the second
                 phase to optimise the placement of facilities in
                 relation to each other Our genetic programming
                 technique is a single phase global optimization
                 strategy using an un constrained tree structure This
                 yields superior results",
  URL =          "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap22.pdf",
  URL =          "http://cognet.mit.edu/library/books/view?isbn=0262611279",
  notes =        "GP-96",
}

Genetic Programming entries for Jaime Garces-Perez Dale A Schoenefeld Roger L Wainwright

Citations