Heuristics for Large Strip Packing Problems with Guillotine Patterns: An Empirical Study

  • Christine L. Mumford-Valenzuela
  • Janis Vick
  • Pearl Y. Wang
Part of the Applied Optimization book series (APOP, volume 86)


In this paper, we undertake an empirical study which examines the effectiveness of eight simple strip packing heuristics on data sets of different sizes with various characteristics and known optima. We restrict this initial study to techniques that produce guillotine patterns (also known as slicing floor plans) which are important industrially. Our chosen heuristics are simple to code, have very fast execution times, and provide a good starting point for our research. In particular, we examine the performance of the eight heuristics as the problems become larger, and demonstrate the effectiveness of a preprocessing routine that rotates some of the rectangles by 90 degrees before the heuristics are applied. We compare the heuristic results to those obtained by using a good genetic algorithm (GA) that also produces guillotine patterns. Our findings suggest that the GA is better on problems of up to about 200 rectangles, but thereafter certain of the heuristics become increasingly effective as the problem size becomes larger, producing better results much more quickly than the GA.


Strip packing Heuristics Genetic algorithm. 


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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Christine L. Mumford-Valenzuela
    • 1
  • Janis Vick
    • 2
  • Pearl Y. Wang
    • 2
  1. 1.Department of Computer ScienceCardiff UniversityWalesUK
  2. 2.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

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