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A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides

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  • Guanglin Xu

    (University of Iowa)

  • Samuel Burer

    (University of Iowa)

Abstract

We study two-stage adjustable robust linear programming in which the right-hand sides are uncertain and belong to a convex, compact uncertainty set. This problem is NP-hard, and the affine policy is a popular, tractable approximation. We prove that under standard and simple conditions, the two-stage problem can be reformulated as a copositive optimization problem, which in turn leads to a class of tractable, semidefinite-based approximations that are at least as strong as the affine policy. We investigate several examples from the literature demonstrating that our tractable approximations significantly improve the affine policy. In particular, our approach solves exactly in polynomial time a class of instances of increasing size for which the affine policy admits an arbitrarily large gap.

Suggested Citation

  • Guanglin Xu & Samuel Burer, 2018. "A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides," Computational Optimization and Applications, Springer, vol. 70(1), pages 33-59, May.
    • Handle: RePEc:spr:coopap:v:70:y:2018:i:1:d:10.1007_s10589-017-9974-x
    DOI: 10.1007/s10589-017-9974-x
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    References listed on IDEAS

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    1. Amir Ardestani-Jaafari & Erick Delage, 2016. "Robust Optimization of Sums of Piecewise Linear Functions with Application to Inventory Problems," Operations Research, INFORMS, vol. 64(2), pages 474-494, April.
    2. Gorissen, Bram L. & den Hertog, Dick, 2013. "Robust counterparts of inequalities containing sums of maxima of linear functions," European Journal of Operational Research, Elsevier, vol. 227(1), pages 30-43.
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    7. Alper Atamtürk & Muhong Zhang, 2007. "Two-Stage Robust Network Flow and Design Under Demand Uncertainty," Operations Research, INFORMS, vol. 55(4), pages 662-673, August.
    8. Fonseca, Raquel J. & Rustem, Berç, 2012. "International portfolio management with affine policies," European Journal of Operational Research, Elsevier, vol. 223(1), pages 177-187.
    9. Oğuz Solyalı & Jean-François Cordeau & Gilbert Laporte, 2016. "The Impact of Modeling on Robust Inventory Management Under Demand Uncertainty," Management Science, INFORMS, vol. 62(4), pages 1188-1201, April.
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    Cited by:

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    2. Immanuel Bomze & Markus Gabl, 2021. "Interplay of non-convex quadratically constrained problems with adjustable robust optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(1), pages 115-151, February.
    3. Ning Zhang & Chang Fang, 2020. "Saddle point approximation approaches for two-stage robust optimization problems," Journal of Global Optimization, Springer, vol. 78(4), pages 651-670, December.
    4. Cheng Guo & Merve Bodur & Joshua A. Taylor, 2021. "Copositive Duality for Discrete Markets and Games," Papers 2101.05379, arXiv.org, revised Jan 2021.
    5. Abbas Khademi & Ahmadreza Marandi & Majid Soleimani-damaneh, 2024. "A new dual-based cutting plane algorithm for nonlinear adjustable robust optimization," Journal of Global Optimization, Springer, vol. 89(3), pages 559-595, July.
    6. Omar El Housni & Vineet Goyal, 2021. "On the Optimality of Affine Policies for Budgeted Uncertainty Sets," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 674-711, May.
    7. Jianzhe Zhen & Ahmadreza Marandi & Danique de Moor & Dick den Hertog & Lieven Vandenberghe, 2022. "Disjoint Bilinear Optimization: A Two-Stage Robust Optimization Perspective," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2410-2427, September.
    8. Angelos Georghiou & Angelos Tsoukalas & Wolfram Wiesemann, 2020. "A Primal–Dual Lifting Scheme for Two-Stage Robust Optimization," Operations Research, INFORMS, vol. 68(2), pages 572-590, March.
    9. Areesh Mittal & Can Gokalp & Grani A. Hanasusanto, 2020. "Robust Quadratic Programming with Mixed-Integer Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 201-218, April.
    10. Bomze, Immanuel M. & Gabl, Markus, 2023. "Optimization under uncertainty and risk: Quadratic and copositive approaches," European Journal of Operational Research, Elsevier, vol. 310(2), pages 449-476.
    11. Angelos Georghiou & Daniel Kuhn & Wolfram Wiesemann, 2019. "The decision rule approach to optimization under uncertainty: methodology and applications," Computational Management Science, Springer, vol. 16(4), pages 545-576, October.
    12. Amir Ardestani-Jaafari & Erick Delage, 2021. "Linearized Robust Counterparts of Two-Stage Robust Optimization Problems with Applications in Operations Management," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1138-1161, July.

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