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SDDP for multistage stochastic programs: preprocessing via scenario reduction

Author

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  • Jitka Dupačová

    (Charles University in Prague)

  • Václav Kozmík

    (Charles University in Prague)

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.

Suggested Citation

  • Jitka Dupačová & Václav Kozmík, 2017. "SDDP for multistage stochastic programs: preprocessing via scenario reduction," Computational Management Science, Springer, vol. 14(1), pages 67-80, January.
    • Handle: RePEc:spr:comgts:v:14:y:2017:i:1:d:10.1007_s10287-016-0261-6
    DOI: 10.1007/s10287-016-0261-6
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    References listed on IDEAS

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