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Building and Solving Large-Scale Stochastic Programs on an Affordable Distributed Computing System

Author

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  • Emmanuel Fragnière
  • Jacek Gondzio
  • Jean-Philippe Vial

Abstract

We present an integrated procedure to build and solve big stochastic programming models. The individual components of the system – the modeling language, the solver and the hardware – are easily accessible, or a least affordable to a large audience. The procedure is applied to a simple financial model, which can be expanded to arbitrarily large sizes by enlarging the number of scenarios. We generated a model with one million scenarios, whose deterministic equivalent linear program has 1,111,112 constraints and 2,555,556 variables. We have been able to solve it on the cluster of ten PCs in less than 3 hours. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Emmanuel Fragnière & Jacek Gondzio & Jean-Philippe Vial, 2000. "Building and Solving Large-Scale Stochastic Programs on an Affordable Distributed Computing System," Annals of Operations Research, Springer, vol. 99(1), pages 167-187, December.
    • Handle: RePEc:spr:annopr:v:99:y:2000:i:1:p:167-187:10.1023/a:1019245101545
    DOI: 10.1023/A:1019245101545
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    Cited by:

    1. Emilia Grass & Kathrin Fischer & Antonia Rams, 2020. "An accelerated L-shaped method for solving two-stage stochastic programs in disaster management," Annals of Operations Research, Springer, vol. 284(2), pages 557-582, January.
    2. Jacek Gondzio & Roy Kouwenberg, 2001. "High-Performance Computing for Asset-Liability Management," Operations Research, INFORMS, vol. 49(6), pages 879-891, December.
    3. 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.
    4. 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.
    5. C A Poojari & C Lucas & G Mitra, 2008. "Robust solutions and risk measures for a supply chain planning problem under uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(1), pages 2-12, January.
    6. Fengqi You & Ignacio Grossmann, 2013. "Multicut Benders decomposition algorithm for process supply chain planning under uncertainty," Annals of Operations Research, Springer, vol. 210(1), pages 191-211, November.

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