Teoretický rámec pro stochastické programování

Theoretical Framework for Stochastic Programming

conference paper

en

This paper aims to present a theoretical framework for stochastic programming models, i.e. for optimization models that involve uncertainty. The framework is characterized by two distinctive features: it is based on the notions of a mathematical program and a probability space. Based on these, it allows the common stochastic programming models to be rigorously derived, and hopefully well understood. Such approach is not common in the literature (See Reference), the stochasticity is generally introduced to a deterministic program after it has been build.

Cílem článku je prezenotvat teoretický rámec pro modely stochastického programování, tj. pro optimalizační modely, které zohledňují náhodnost. Rámec má dvě význačné charakteristiky: je založen na pojmech matematického programu a pravděpodobnostního prostoru. Na jejich základě lze rigorózně odvodit a pochopit obvyklé modely stochastického programování. Tento přístup není běžný v obvyklé literatuře (viz Reference), kde je stochastičnost dodatečně přidávána do již zformulovaného deterministického programu.

This paper aims to present a theoretical framework for stochastic programming models, i.e. for optimization models that involve uncertainty. The framework is characterized by two distinctive features: it is based on the notions of a mathematical program and a probability space. Based on these, it allows the common stochastic programming models to be rigorously derived, and hopefully well understood. Such approach is not common in the literature (See Reference), the stochasticity is generally introduced to a deterministic program after it has been build.

Dvojstupňové stochastické programování, Separabilní funkce, Wait-and-see, Here-and-now, Pravděpodobnost

Two-stage stochastic programming, Separable function, Wait-and-see, Here-and-now, Probability

24.06.2009

Brno

978-80-214-3884-2

MENDEL 2009

1

239–246

8