Automating the packing heuristic design process with genetic programming

Edmund K. Burke, Matthew R. Hyde, Graham Kendall, John Woodward

Research output: Journal PublicationArticlepeer-review

64 Citations (Scopus)
9 Downloads (Pure)

Abstract

The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.
Original languageEnglish
Pages (from-to)63-89
JournalEvolutionary Computation
Volume20
Issue number1
Early online date23 Feb 2012
DOIs
Publication statusPublished Online - 23 Feb 2012

Keywords

  • Genetic programming
  • cutting and packing
  • evolutionary design
  • genetic algorithms
  • hyper-heuristics

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