Computational Management Science

, Volume 14, Issue 1, pp 67–80

SDDP for multistage stochastic programs: preprocessing via scenario reduction

Original Paper

Abstract

Even with recent enhancements, computation times for large-scale multistage problems with risk-averse objective functions can be very long. Therefore, preprocessing via scenario reduction could be considered as a way to significantly improve the overall performance. Stage-wise backward reduction of single scenarios applied to a fixed branching structure of the tree is a promising tool for efficient algorithms like stochastic dual dynamic programming. We provide computational results which show an acceptable precision of the results for the reduced problem and a substantial decrease of the total computation time.

Keywords

Multistage stochastic programs Stochastic dual dynamic programming Multiperiod CVaR Scenario reduction 

Mathematics Subject Classification

65C05 90C15 91G60 

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Probability and Mathematical StatisticsCharles University in PraguePragueCzech Republic

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