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Personne :
Lamond, Bernard

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Lamond

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Bernard

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Université Laval. Département d'opérations et systèmes de décision

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ncf10119525

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Voici les éléments 1 - 3 sur 3
  • PublicationAccès libre
    Controlled approximation of the value function in stochastic dynamic programming for multi-reservoir systems
    (Springer, 2015-10-01) Zéphyr, Luckny; Lang, Pascal; Lamond, Bernard
    We present a new approach for adaptive approximation of the value function in stochastic dynamic programming. Under convexity assumptions, our method is based on a simplicial partition of the state space. Bounds on the value function provide guidance as to where refinement should be done, if at all. Thus, the method allows for a trade-off between solution time and accuracy. The proposed scheme is experimented in the particular context of hydroelectric production across multiple reservoirs.
  • PublicationAccès libre
    Approximate stochastic dynamic programming for hydroelectric production planning
    (Elsevier, 2017-03-23) Zéphyr, Luckny; Côté, Pascal; Lang, Pascal; Lamond, Bernard
    This paper presents a novel approach for approximate stochastic dynamic programming (ASDP) over a continuous state space when the optimization phase has a near-convex structure. The approach entails a simplicial partitioning of the state space. Bounds on the true value function are used to refine the partition. We also provide analytic formulae for the computation of the expectation of the value function in the “uni-basin” case where natural inflows are strongly correlated. The approach is experimented on several configurations of hydro-energy systems. It is also tested against actual industrial data.
  • PublicationAccès libre
    Note on “Parameters estimators of irregular right-angled triangular distribution”
    (IOS Press, 2021-12-20) Zéphyr, Luckny; Lamond, Bernard
    Simple estimators were given in (Kachiashvili & Topchishvili, 2016) for the lower and upper limits of an irregular right-angled triangular distribution together with convenient formulas for removing their bias. We argue here that the smallest observation is not a maximum likelihood estimator (MLE) of the lower limit and we present a procedure for computing an MLE of this parameter. We show that the MLE is strictly smaller than the smallest observation and we give some bounds that are useful in a numerical solution procedure. We also present simulation results to assess the bias and variance of the MLE.