Reference : Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine ...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/130529
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
English
Defourny, Boris [Princeton University > Department of Operations Research and Financial Engineering > > >]
Ernst, Damien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2013
INFORMS Journal on Computing
INFORMS: Institute for Operations Research
25
3
488-501
Yes (verified by ORBi)
International
1091-9856
[en] Stochastic Programming ; Machine Learning
[en] In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree solution and are selected by out-of-sample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution (policy) found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain near-optimal solutions, starting with a “weak” scenario-tree generator that randomizes the branching structure of the trees.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/130529
10.1287/ijoc.1120.0516

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