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Dynamic sequencing and cut consolidation for the parallel hybrid-cut nested L-shaped method

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  • Wolf, Christian
  • Koberstein, Achim

Abstract

The nested L-shaped method is used to solve two- and multi-stage linear stochastic programs with recourse, which can have integer variables on the first stage. In this paper we present and evaluate a cut consolidation technique and a dynamic sequencing protocol to accelerate the solution process. Furthermore, we present a parallelized implementation of the algorithm, which is developed within the COIN-OR framework. We show on a test set of 51 two-stage and 42 multi-stage problems, that both of the developed techniques lead to significant speed ups in computation time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:230:y:2013:i:1:p:143-156
    DOI: 10.1016/j.ejor.2013.04.017
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Placido dos Santos, Felipe Silva & Oliveira, Fabricio, 2019. "An enhanced L-Shaped method for optimizing periodic-review inventory control problems modeled via two-stage stochastic programming," European Journal of Operational Research, Elsevier, vol. 275(2), pages 677-693.
    3. Hongtao Tang & Xixing Li & Shunsheng Guo & Shuwei Liu & Li Li & Lang Huang, 2017. "An optimizing model to solve the nesting problem of rectangle pieces based on genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1817-1826, December.
    4. 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.
    5. 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.
    6. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    7. Rodríguez, Jesús A. & Anjos, Miguel F. & Côté, Pascal & Desaulniers, Guy, 2021. "Accelerating Benders decomposition for short-term hydropower maintenance scheduling," European Journal of Operational Research, Elsevier, vol. 289(1), pages 240-253.
    8. Weskamp, Christoph & Koberstein, Achim & Schwartz, Frank & Suhl, Leena & Voß, Stefan, 2019. "A two-stage stochastic programming approach for identifying optimal postponement strategies in supply chains with uncertain demand," Omega, Elsevier, vol. 83(C), pages 123-138.
    9. Lin, Dung-Ying & Kuo, Jia-Kai, 2021. "The vehicle deployment and relocation problem for electric vehicle sharing systems considering demand and parking space stochasticity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    10. 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.

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