Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
Year: 2012
Type: article
Abstract: 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.
Source: INFORMS journal on computing
Authors: Boris Defourny, Damien Ernst, Louis Wehenkel
Institutions: Princeton University, University of Liège
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2154716504