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On Solving Multistage Stochastic Programs with Coherent Risk Measures

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  1. Václav Kozmík, 2015. "On variance reduction of mean-CVaR Monte Carlo estimators," Computational Management Science, Springer, vol. 12(2), pages 221-242, April.
  2. de Queiroz, Anderson Rodrigo, 2016. "Stochastic hydro-thermal scheduling optimization: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 382-395.
  3. Abate, Arega Getaneh & Riccardi, Rossana & Ruiz, Carlos, 2021. "Contracts in electricity markets under EU ETS: A stochastic programming approach," Energy Economics, Elsevier, vol. 99(C).
  4. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
  5. Daniel R. Jiang & Warren B. Powell, 2018. "Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures," Mathematics of Operations Research, INFORMS, vol. 43(2), pages 554-579, May.
  6. Guigues, Vincent & Shapiro, Alexander & Cheng, Yi, 2023. "Duality and sensitivity analysis of multistage linear stochastic programs," European Journal of Operational Research, Elsevier, vol. 308(2), pages 752-767.
  7. Lorenzo Reus & Guillermo Alexander Sepúlveda-Hurtado, 2023. "Foreign exchange trading and management with the stochastic dual dynamic programming method," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
  8. Weini Zhang & Hamed Rahimian & Güzin Bayraksan, 2016. "Decomposition Algorithms for Risk-Averse Multistage Stochastic Programs with Application to Water Allocation under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 385-404, August.
  9. Haodong Yu & Jie Sun & Yanjun Wang, 2021. "A time-consistent Benders decomposition method for multistage distributionally robust stochastic optimization with a scenario tree structure," Computational Optimization and Applications, Springer, vol. 79(1), pages 67-99, May.
  10. 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.
  11. 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.
  12. Dan A. Iancu & Marek Petrik & Dharmashankar Subramanian, 2015. "Tight Approximations of Dynamic Risk Measures," Mathematics of Operations Research, INFORMS, vol. 40(3), pages 655-682, March.
  13. Bäuerle, Nicole & Glauner, Alexander, 2022. "Markov decision processes with recursive risk measures," European Journal of Operational Research, Elsevier, vol. 296(3), pages 953-966.
  14. Pedro Borges, 2022. "Cut-sharing across trees and efficient sequential sampling for SDDP with uncertainty in the RHS," Computational Optimization and Applications, Springer, vol. 82(3), pages 617-647, July.
  15. Löhndorf, Nils & Wozabal, David, 2021. "Gas storage valuation in incomplete markets," European Journal of Operational Research, Elsevier, vol. 288(1), pages 318-330.
  16. Reus, Lorenzo & Pagnoncelli, Bernardo & Armstrong, Margaret, 2019. "Better management of production incidents in mining using multistage stochastic optimization," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  17. Moret, Fabio & Pinson, Pierre & Papakonstantinou, Athanasios, 2020. "Heterogeneous risk preferences in community-based electricity markets," European Journal of Operational Research, Elsevier, vol. 287(1), pages 36-48.
  18. Joakim Dimoski & Stein-Erik Fleten & Nils Löhndorf & Sveinung Nersten, 2023. "Dynamic hedging for the real option management of hydropower production with exchange rate risks," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 525-554, June.
  19. Volker Krätschmer & Marcel Ladkau & Roger J. A. Laeven & John G. M. Schoenmakers & Mitja Stadje, 2018. "Optimal Stopping Under Uncertainty in Drift and Jump Intensity," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1177-1209, November.
  20. 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.
  21. Bernardo K. Pagnoncelli & Adriana Piazza, 2017. "The optimal harvesting problem under price uncertainty: the risk averse case," Annals of Operations Research, Springer, vol. 258(2), pages 479-502, November.
  22. Ignacio Marín Cruz & Mohamed Badaoui & Ricardo Mota Palomino, 2023. "Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico," Energies, MDPI, vol. 16(17), pages 1-26, August.
  23. Rudloff, Birgit & Street, Alexandre & Valladão, Davi M., 2014. "Time consistency and risk averse dynamic decision models: Definition, interpretation and practical consequences," European Journal of Operational Research, Elsevier, vol. 234(3), pages 743-750.
  24. Alois Pichler, 2017. "A quantitative comparison of risk measures," Annals of Operations Research, Springer, vol. 254(1), pages 251-275, July.
  25. 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.
  26. Alois Pichler & Ruben Schlotter, 2020. "Quantification of Risk in Classical Models of Finance," Papers 2004.04397, arXiv.org, revised Feb 2021.
  27. Lorenzo Reus & Rodolfo Prado, 2022. "Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 47-69, June.
  28. Borgonovo, E. & Cappelli, V. & Maccheroni, F. & Marinacci, M., 2018. "Risk analysis and decision theory: A bridge," European Journal of Operational Research, Elsevier, vol. 264(1), pages 280-293.
  29. Dominic White & Niven Winchester, 2018. "Energy- and multi-sector modelling of climate change mitigation in New Zealand: current practice and future needs," Working Papers 18_15, Motu Economic and Public Policy Research.
  30. Park, Jangho & Bayraksan, Güzin, 2023. "A multistage distributionally robust optimization approach to water allocation under climate uncertainty," European Journal of Operational Research, Elsevier, vol. 306(2), pages 849-871.
  31. Liu, Rui Peng & Shapiro, Alexander, 2020. "Risk neutral reformulation approach to risk averse stochastic programming," European Journal of Operational Research, Elsevier, vol. 286(1), pages 21-31.
  32. A. B. Philpott & V. L. Matos & L. Kapelevich, 2018. "Distributionally robust SDDP," Computational Management Science, Springer, vol. 15(3), pages 431-454, October.
  33. Thuener Silva & Davi Valladão & Tito Homem-de-Mello, 2021. "A data-driven approach for a class of stochastic dynamic optimization problems," Computational Optimization and Applications, Springer, vol. 80(3), pages 687-729, December.
  34. 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.
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