Displaying similar documents to “Risk objectives in two-stage stochastic programming models”

Bound-based decision rules in multistage stochastic programming

Daniel Kuhn, Panos Parpas, Berç Rustem (2008)

Kybernetika

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We study bounding approximations for a multistage stochastic program with expected value constraints. Two simpler approximate stochastic programs, which provide upper and lower bounds on the original problem, are obtained by replacing the original stochastic data process by finitely supported approximate processes. We model the original and approximate processes as dependent random vectors on a joint probability space. This probabilistic coupling allows us to transform the optimal solution...

Multistage stochastic programs via autoregressive sequences and individual probability constraints

Vlasta Kaňková (2008)

Kybernetika

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The paper deals with a special case of multistage stochastic programming problems. In particular, the paper deals with multistage stochastic programs in which a random element follows an autoregressive sequence and constraint sets correspond to the individual probability constraints. The aim is to investigate a stability (considered with respect to a probability measures space) and empirical estimates. To achieve new results the Wasserstein metric determined by ℒ 1 norm and results of...

A two-stage stochastic optimization model for a gas sale retailer

F. Maggioni, Maria Teresa Vespucci, E. Allevi, Marida Bertocchi, M. Innorta (2008)

Kybernetika

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The paper deals with a new stochastic optimization model, named OMoGaS–SV (Optimization Modelling for Gas Seller–Stochastic Version), to assist companies dealing with gas retail commercialization. Stochasticity is due to the dependence of consumptions on temperature uncertainty. Due to nonlinearities present in the objective function, the model can be classified as an NLP mixed integer model, with the profit function depending on the number of contracts with the final consumers, the...