Short-term hydropower production planning by stochastic programming
Computers and Operations Research
Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Scenario reduction techniques in stochastic programming
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Bounds for multistage stochastic programs using supervised learning strategies
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Evolutionary multi-stage financial scenario tree generation
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
INFORMS Journal on Computing
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This paper presents procedures for constructing numerically solvable discretizations of multistage stochastic programs that epi-converge to the original problem as the discretizations are made finer. Epi-convergence implies, in particular, that the cluster points of the first-stage solutions of the discretized problems are optimal first-stage solutions of the original problem. The discretization procedures apply to a general class of nonlinear stochastic programs where the uncertain factors are driven by time series models. Using existing routines for numerical integration allows for an easy and efficient implementation of the procedures.