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Applying oracles of on-demand accuracy in two-stage stochastic programming – A computational study

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  • Wolf, Christian
  • Fábián, Csaba I.
  • Koberstein, Achim
  • Suhl, Leena

Abstract

Traditionally, two variants of the L-shaped method based on Benders’ decomposition principle are used to solve two-stage stochastic programming problems: the aggregate and the disaggregate version. In this study we report our experiments with a special convex programming method applied to the aggregate master problem. The convex programming method is of the type that uses an oracle with on-demand accuracy. We use a special form which, when applied to two-stage stochastic programming problems, is shown to integrate the advantages of the traditional variants while avoiding their disadvantages. On a set of 105 test problems, we compare and analyze parallel implementations of regularized and unregularized versions of the algorithms. The results indicate that solution times are significantly shortened by applying the concept of on-demand accuracy.

Suggested Citation

  • Wolf, Christian & Fábián, Csaba I. & Koberstein, Achim & Suhl, Leena, 2014. "Applying oracles of on-demand accuracy in two-stage stochastic programming – A computational study," European Journal of Operational Research, Elsevier, vol. 239(2), pages 437-448.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:2:p:437-448
    DOI: 10.1016/j.ejor.2014.05.010
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    References listed on IDEAS

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    1. Trukhanov, Svyatoslav & Ntaimo, Lewis & Schaefer, Andrew, 2010. "Adaptive multicut aggregation for two-stage stochastic linear programs with recourse," European Journal of Operational Research, Elsevier, vol. 206(2), pages 395-406, October.
    2. Wolf, Christian & Koberstein, Achim, 2013. "Dynamic sequencing and cut consolidation for the parallel hybrid-cut nested L-shaped method," European Journal of Operational Research, Elsevier, vol. 230(1), pages 143-156.
    3. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
    4. K. A. Ariyawansa & Andrew J. Felt, 2004. "On a New Collection of Stochastic Linear Programming Test Problems," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 291-299, August.
    5. Wim Ackooij & Welington Oliveira, 2014. "Level bundle methods for constrained convex optimization with various oracles," Computational Optimization and Applications, Springer, vol. 57(3), pages 555-597, April.
    6. Birge, John R. & Louveaux, Francois V., 1988. "A multicut algorithm for two-stage stochastic linear programs," European Journal of Operational Research, Elsevier, vol. 34(3), pages 384-392, March.
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    Cited by:

    1. Wim Ackooij & Welington Oliveira & Yongjia Song, 2019. "On level regularization with normal solutions in decomposition methods for multistage stochastic programming problems," Computational Optimization and Applications, Springer, vol. 74(1), pages 1-42, September.
    2. Li, Mo & Guo, Ping, 2015. "A coupled random fuzzy two-stage programming model for crop area optimization—A case study of the middle Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 155(C), pages 53-66.
    3. Blanchot, Xavier & Clautiaux, François & Detienne, Boris & Froger, Aurélien & Ruiz, Manuel, 2023. "The Benders by batch algorithm: Design and stabilization of an enhanced algorithm to solve multicut Benders reformulation of two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 309(1), pages 202-216.
    4. W. Ackooij & A. Frangioni & W. Oliveira, 2016. "Inexact stabilized Benders’ decomposition approaches with application to chance-constrained problems with finite support," Computational Optimization and Applications, Springer, vol. 65(3), pages 637-669, December.
    5. Pavlo Glushko & Csaba I. Fábián & Achim Koberstein, 2022. "An L-shaped method with strengthened lift-and-project cuts," Computational Management Science, Springer, vol. 19(4), pages 539-565, October.
    6. Göke, Leonard & Schmidt, Felix & Kendziorski, Mario, 2024. "Stabilized Benders decomposition for energy planning under climate uncertainty," European Journal of Operational Research, Elsevier, vol. 316(1), pages 183-199.
    7. Mínguez, R. & van Ackooij, W. & García-Bertrand, R., 2021. "Constraint generation for risk averse two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 288(1), pages 194-206.
    8. Babak Saleck Pay & Yongjia Song, 2020. "Partition-based decomposition algorithms for two-stage Stochastic integer programs with continuous recourse," Annals of Operations Research, Springer, vol. 284(2), pages 583-604, January.
    9. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.
    10. Wim van Ackooij & Welington de Oliveira & Yongjia Song, 2018. "Adaptive Partition-Based Level Decomposition Methods for Solving Two-Stage Stochastic Programs with Fixed Recourse," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 57-70, February.
    11. Luisa I. Martínez-Merino & Diego Ponce & Justo Puerto, 2023. "Constraint relaxation for the discrete ordered median problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 538-561, October.

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