Solving Facility Layout Problems Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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