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Two-stage stochastic problems with correlated normal variables: computational experiences

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  • István Deák

Abstract

Two-stage models are frequently used when making decisions under the influence of randomness. The case of normally distributed right hand side vector – with independent or correlated components – is treated here. The expected recourse function is computed by an enhanced Monte Carlo integration technique. Successive regression approximation technique is used for computing the optimal solution of the problem. Computational issues of the algorithm are discussed, improvements are proposed and numerical results are presented for random right hand side and a random matrix in the second stage problems. Copyright Springer Science + Business Media, Inc. 2006

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  • István Deák, 2006. "Two-stage stochastic problems with correlated normal variables: computational experiences," Annals of Operations Research, Springer, vol. 142(1), pages 79-97, February.
    • Handle: RePEc:spr:annopr:v:142:y:2006:i:1:p:79-97:10.1007/s10479-006-6162-2
    DOI: 10.1007/s10479-006-6162-2
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    Cited by:

    1. Clara Lage & Claudia Sagastizábal & Mikhail Solodov, 2019. "Multiplier Stabilization Applied to Two-Stage Stochastic Programs," Journal of Optimization Theory and Applications, Springer, vol. 183(1), pages 158-178, October.
    2. Clara Lage & Claudia Sagastizábal & Mikhail Solodov, 2020. "Multiplier Stabilization Applied to Two-Stage Stochastic Programs," Post-Print halshs-02900862, HAL.
    3. Clara Lage & Claudia Sagastizábal & Mikhail Solodov, 2020. "Multiplier Stabilization Applied to Two-Stage Stochastic Programs," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02900862, HAL.
    4. Clara Lage & Claudia Sagastizábal & Mikhail Solodov, 2020. "Multiplier Stabilization Applied to Two-Stage Stochastic Programs," Documents de travail du Centre d'Economie de la Sorbonne 20010, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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