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Scenario decomposition of risk-averse multistage stochastic programming problems

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  • Ricardo Collado
  • Dávid Papp
  • Andrzej Ruszczyński

Abstract

For a risk-averse multistage stochastic optimization problem with a finite scenario tree, we introduce a new scenario decomposition method and we prove its convergence. The main idea of the method is to construct a family of risk-neutral approximations of the problem. The method is applied to a risk-averse inventory and assembly problem. In addition, we develop a partially regularized bundle method for nonsmooth optimization. Copyright Springer Science+Business Media, LLC 2012

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  • Ricardo Collado & Dávid Papp & Andrzej Ruszczyński, 2012. "Scenario decomposition of risk-averse multistage stochastic programming problems," Annals of Operations Research, Springer, vol. 200(1), pages 147-170, November.
    • Handle: RePEc:spr:annopr:v:200:y:2012:i:1:p:147-170:10.1007/s10479-011-0935-y
    DOI: 10.1007/s10479-011-0935-y
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    Cited by:

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    2. Homem-de-Mello, Tito & Pagnoncelli, Bernardo K., 2016. "Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective," European Journal of Operational Research, Elsevier, vol. 249(1), pages 188-199.
    3. Mahmutoğulları, Ali İrfan & Çavuş, Özlem & Aktürk, M. Selim, 2018. "Bounds on risk-averse mixed-integer multi-stage stochastic programming problems with mean-CVaR," European Journal of Operational Research, Elsevier, vol. 266(2), pages 595-608.
    4. Alonso-Ayuso, Antonio & Escudero, Laureano F. & Guignard, Monique & Weintraub, Andres, 2018. "Risk management for forestry planning under uncertainty in demand and prices," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1051-1074.
    5. Andre Luiz Diniz & Maria Elvira P. Maceira & Cesar Luis V. Vasconcellos & Debora Dias J. Penna, 2020. "A combined SDDP/Benders decomposition approach with a risk-averse surface concept for reservoir operation in long term power generation planning," Annals of Operations Research, Springer, vol. 292(2), pages 649-681, September.
    6. Sun, Ran & Fan, Yueyue, 2024. "Stochastic OD demand estimation using stochastic programming," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    7. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    8. Schur, Rouven & Gönsch, Jochen & Hassler, Michael, 2019. "Time-consistent, risk-averse dynamic pricing," European Journal of Operational Research, Elsevier, vol. 277(2), pages 587-603.
    9. Alois Pichler, 2017. "A quantitative comparison of risk measures," Annals of Operations Research, Springer, vol. 254(1), pages 251-275, July.
    10. Malekipirbazari, Milad & Çavuş, Özlem, 2024. "Index policy for multiarmed bandit problem with dynamic risk measures," European Journal of Operational Research, Elsevier, vol. 312(2), pages 627-640.
    11. 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.
    12. Collado, Ricardo & Meisel, Stephan & Priekule, Laura, 2017. "Risk-averse stochastic path detection," European Journal of Operational Research, Elsevier, vol. 260(1), pages 195-211.
    13. Jonathan Eckstein & Deniz Eskandani & Jingnan Fan, 2016. "Multilevel Optimization Modeling for Risk-Averse Stochastic Programming," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 112-128, February.

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