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Controlled Approximation of the Stochastic Dynamic Programming Value Function for Multi-Reservoir Systems

In: Computational Management Science

Author

Listed:
  • Luckny Zéphyr

    (Université Laval, Pavillon Palasis-Prince)

  • Pascal Lang

    (Université Laval, Pavillon Palasis-Prince)

  • Bernard F. Lamond

    (Université Laval, Pavillon Palasis-Prince)

  • Pascal Côté

    (Rio Tinto Alcan, Énergie électrique)

Abstract

We present an approximation of the Stochastic Dynamic Programming (SDP) value function based on a partition of the state space into simplices. The vertices of such simplices form an irregular grid over which the value function is computed. Under convexity assumptions, lower and upper bounds are developed over the state space continuum. The partition is then refined where the gap between these bounds is largest. This process readily provides a controllable trade-off between accuracy and solution time.

Suggested Citation

  • Luckny Zéphyr & Pascal Lang & Bernard F. Lamond & Pascal Côté, 2016. "Controlled Approximation of the Stochastic Dynamic Programming Value Function for Multi-Reservoir Systems," Lecture Notes in Economics and Mathematical Systems, in: Raquel J. Fonseca & Gerhard-Wilhelm Weber & João Telhada (ed.), Computational Management Science, edition 1, pages 31-37, Springer.
    • Handle: RePEc:spr:lnechp:978-3-319-20430-7_5
    DOI: 10.1007/978-3-319-20430-7_5
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