Solving the Two-Dimensional Bin-Packing Problem with Variable Bin Sizes by Greedy Randomized Adaptive Search Procedures and Variable Neighborhood Search

  • Andreas M. Chwatal
  • Sandro Pirkwieser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Abstract

In this work we present new metaheuristic algorithms to a special variant of the two-dimensional bin-packing, or cutting-stock problem, where a given set of rectangular items (demand) must be produced out of heterogeneous stock items (bins). The items can optionally be rotated, guillotine-cuttable and free layouts are considered. The proposed algorithms use various packing-heuristics which are embedded in a greedy randomized adaptive search procedure (GRASP) and variable neighborhood search (VNS) framework. Our results for existing benchmark-instances show the superior performance of our algorithms, in particular the VNS, with respect to previous approaches.

Keywords

Packing Problem Variable Neighborhood Search Metaheuristic Algorithm Construction Heuristic Variable Neighborhood Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andreas M. Chwatal
    • 1
  • Sandro Pirkwieser
    • 1
    • 2
  1. 1.Destion – IT Consulting OGViennaAustria
  2. 2.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria

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