Algorithms and data structures for three-dimensional packing

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Allen:thesis,
  author =       "Sam D. Allen",
  title =        "Algorithms and data structures for three-dimensional
                 packing",
  school =       "School of Computer Science, University of Nottingham",
  year =         "2011",
  address =      "UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, packing,
                 shipment, business, operations research",
  URL =          "http://etheses.nottingham.ac.uk/2779/1/thesis_nicer.pdf",
  size =         "123 pages",
  abstract =     "Cutting and packing problems are increasingly
                 prevalent in industry. A well used freight vehicle will
                 save a business money when delivering goods, as well as
                 reducing the environmental impact, when compared to
                 sending out two lesser-used freight vehicles. A cutting
                 machine that generates less wasted material will have a
                 similar effect. Industry reliance on automating these
                 processes and improving productivity is increasing
                 year-on-year.

                 This thesis presents a number of methods for generating
                 high quality solutions for these cutting and packing
                 challenges. It does so in a number of ways. A fast,
                 efficient framework for heuristically generating
                 solutions to large problems is presented, and a method
                 of incrementally improving these solutions over time is
                 implemented and shown to produce even higher packing.
                 The results from these findings provide the best known
                 results for 28 out of 35 problems from the literature.
                 This framework is analysed and its effectiveness shown
                 over a number of datasets, along with a discussion of
                 its theoretical suitability for higher-dimensional
                 packing problems. A way of automatically generating new
                 heuristics for this framework that can be problem
                 specific, and therefore highly tuned to a given
                 dataset, is then demonstrated and shown to perform well
                 when compared to the expert-designed packing
                 heuristics. Finally some mathematical models which can
                 guarantee the optimality of packings for small datasets
                 are given, and the (in)effectiveness of these
                 techniques discussed. The models are then strengthened
                 and a novel model presented which can handle much
                 larger problems under certain conditions. The thesis
                 finishes with a discussion about the applicability of
                 the different approaches taken to the real-world
                 problems that motivate them.",
  notes =        "Supervisors: Edmund K. Burke and Graham Kendall

                 ID Code: 2779",
}

Genetic Programming entries for Sam D Allen

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