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Optimization-Driven Scenario Grouping

Author

Listed:
  • Kevin Ryan

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Shabbir Ahmed

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Santanu S. Dey

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Deepak Rajan

    (Lawrence Livermore National Laboratory, Livermore, California 94550)

  • Amelia Musselman

    (Lawrence Livermore National Laboratory, Livermore, California 94550)

  • Jean-Paul Watson

    (Sandia National Laboratory, Livermore, California 94550)

Abstract

Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. We develop an approach that improves on these bounds by reinforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into groups . Specifically, we formulate an optimization problem for grouping scenarios that aims to improve the bound by optimizing a proxy metric based on information obtained from evaluating a subset of candidate feasible solutions. We show that the proposed grouping problem is NP-hard in general, identify a polynomially solvable case, and present two formulations for solving the problem: a matching formulation for a special case and a mixed-integer programming formulation for the general case. We use the proposed grouping scheme as a preprocessing step for a particular scenario decomposition algorithm and demonstrate its effectiveness in solving standard test instances of two-stage 0–1 stochastic programs. Using this approach, we are able to prove optimality for all previously unsolved instances of a standard test set. Additionally, we implement this scheme as a preprocessing step for PySP, a publicly available and widely used implementation of progressive hedging, and compare this grouping approach with standard grouping approaches on large-scale stochastic unit commitment instances. Finally, the idea is extended to propose a finitely convergent algorithm for two-stage stochastic programs with a finite feasible region.

Suggested Citation

  • Kevin Ryan & Shabbir Ahmed & Santanu S. Dey & Deepak Rajan & Amelia Musselman & Jean-Paul Watson, 2020. "Optimization-Driven Scenario Grouping," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 805-821, July.
    • Handle: RePEc:inm:orijoc:v:32:y:3:i:2020:p:805-821
    DOI: 10.1287/ijoc.2019.0924
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    References listed on IDEAS

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    1. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    2. Yan Deng & Shabbir Ahmed & Siqian Shen, 2018. "Parallel Scenario Decomposition of Risk-Averse 0-1 Stochastic Programs," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 90-105, February.
    3. Jean-Paul Watson & David Woodruff, 2011. "Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems," Computational Management Science, Springer, vol. 8(4), pages 355-370, November.
    4. Gustavo Angulo & Shabbir Ahmed & Santanu S. Dey, 2016. "Improving the Integer L-Shaped Method," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 483-499, August.
    5. Francesca Maggioni & Elisabetta Allevi & Marida Bertocchi, 2016. "Monotonic bounds in multistage mixed-integer stochastic programming," Computational Management Science, Springer, vol. 13(3), pages 423-457, July.
    6. Santanu S. Dey & Marco Molinaro & Qianyi Wang, 2018. "Analysis of Sparse Cutting Planes for Sparse MILPs with Applications to Stochastic MILPs," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 304-332, February.
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    Cited by:

    1. Wei Zhang & Kai Wang & Alexandre Jacquillat & Shuaian Wang, 2023. "Optimized Scenario Reduction: Solving Large-Scale Stochastic Programs with Quality Guarantees," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 886-908, July.
    2. Jiang, Xiaoping & Bai, Ruibin & Ren, Jianfeng & Li, Jiawei & Kendall, Graham, 2022. "Lagrange dual bound computation for stochastic service network design," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1097-1112.

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