SDDP for multistage stochastic programs: preprocessing via scenario reduction

详细信息    查看全文

• 作者：Jitka Dupačová ; Václav Kozmík
• 关键词：Multistage stochastic programs ; Stochastic dual dynamic programming ; Multiperiod CVaR ; Scenario reduction
• 刊名：Computational Management Science
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：14
• 期：1
• 页码：67-80
• 全文大小：
• 刊物主题：Operation Research/Decision Theory; Optimization;
• 出版者：Springer Berlin Heidelberg
• ISSN：1619-6988
• 卷排序：14

文摘

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.