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SLOPPGEN: A Problem Generator for the Two-Dimensional Rectangular Single Large Object Placement Problem With a Single Defect

Author

Listed:
  • Vera Neidlein

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

  • Gerhard Wäscher

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

Abstract

In this paper, a problem generator for the Two-Dimensional Rectangular Single Large Object Placement Problem is presented. The parameters defining this problem are identified and described. The fea-tures of the problem generator are pointed out, and it is shown how the program can be used for the generation of reproducible random problem instances.

Suggested Citation

  • Vera Neidlein & Gerhard Wäscher, 2008. "SLOPPGEN: A Problem Generator for the Two-Dimensional Rectangular Single Large Object Placement Problem With a Single Defect," FEMM Working Papers 08015, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    • Handle: RePEc:mag:wpaper:08015
    as

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    File URL: http://www.ww.uni-magdeburg.de/fwwdeka/femm/a2008_Dateien/2008_15.pdf
    File Function: First version, 2008
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    References listed on IDEAS

    as
    1. Nicos Christofides & Charles Whitlock, 1977. "An Algorithm for Two-Dimensional Cutting Problems," Operations Research, INFORMS, vol. 25(1), pages 30-44, February.
    2. Gau, T. & Wascher, G., 1995. "CUTGEN1: A problem generator for the standard one-dimensional cutting stock problem," European Journal of Operational Research, Elsevier, vol. 84(3), pages 572-579, August.
    3. P. C. Gilmore & R. E. Gomory, 1965. "Multistage Cutting Stock Problems of Two and More Dimensions," Operations Research, INFORMS, vol. 13(1), pages 94-120, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Silva, Elsa & Oliveira, José F. & Wäscher, Gerhard, 2014. "2DCPackGen: A problem generator for two-dimensional rectangular cutting and packing problems," European Journal of Operational Research, Elsevier, vol. 237(3), pages 846-856.
    2. Gonçalves, José Fernando & Wäscher, Gerhard, 2020. "A MIP model and a biased random-key genetic algorithm based approach for a two-dimensional cutting problem with defects," European Journal of Operational Research, Elsevier, vol. 286(3), pages 867-882.

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