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A Scalable Approach for the K-Staged Two-Dimensional Cutting Stock Problem

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Operations Research Proceedings 2015

Part of the book series: Operations Research Proceedings ((ORP))

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Abstract

This work focuses on the K-staged two-dimensional cutting stock problem with variable sheet size. High-quality solutions are computed by an efficient beam-search algorithm that exploits the congruency of subpatterns and takes informed decisions on which of the available sheet types to use for the solutions. We extend this algorithm by embedding it in a sequential value-correction framework that runs the algorithm multiple times while adapting element type values in each iteration and thus constitutes a guided diversification process for computing a solution. Experiments demonstrate the effectiveness of the approach and that the sequential value-correction further increases the overall quality of the constructed solutions.

We thank LodeStar Technology for their support and collaboration in this project.

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Authors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Tu Wien, Vienna, Austria

    Frederico Dusberger & Günther R. Raidl

Authors
  1. Frederico Dusberger
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  2. Günther R. Raidl
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Corresponding author

Correspondence to Frederico Dusberger .

Editor information

Editors and Affiliations

  1. Department of Business Administration, University of Vienna, Vienna, Austria

    Karl Franz Dörner

  2. Decision Sciences and Statistics (IDS) Department, ESSEC Business School of Paris, Cergy Pontoise Cedex, France

    Ivana Ljubic

  3. Department of Statistics and Operations Research, University of Vienna, Vienna, Austria

    Georg Pflug

  4. Operations Research and Control Systems, TU Wien, Vienna, Austria

    Gernot Tragler

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Dusberger, F., Raidl, G.R. (2017). A Scalable Approach for the K-Staged Two-Dimensional Cutting Stock Problem. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_52

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