A Note on Optimization under Uncertainty: Comparing Probabilistically Constrained and Robust Optimization Methodology

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Note on Optimization under Uncertainty: Comparing Probabilistically Constrained and Robust Optimization Methodology

Popis výsledku v původním jazyce

Dealing with optimization problems, data entering the optimization process are usually of uncertain nature. There are several approaches to deal with data uncertainty, starting with classical sensitivity analysis and/or parametric programming. In this paper, we concentrate on two specific approaches, namely on chance constrained (stochastic) and robust optimization. Chance (probabilistically) constrained optimization is based on the assumption that underlying uncertainty is driven by a probability distribution, that is, considered as a random vector. On the other hand, robust optimization deals with the situation in which uncertainty is given only by a membership of the uncertain factor to an explicitly defined set. We compare these two approaches with respect to the behavior of optimal values and optimal solution sets and discuss the drawbacks of each of two approaches on an illustrative example.

Název v anglickém jazyce

A Note on Optimization under Uncertainty: Comparing Probabilistically Constrained and Robust Optimization Methodology

Popis výsledku anglicky

Dealing with optimization problems, data entering the optimization process are usually of uncertain nature. There are several approaches to deal with data uncertainty, starting with classical sensitivity analysis and/or parametric programming. In this paper, we concentrate on two specific approaches, namely on chance constrained (stochastic) and robust optimization. Chance (probabilistically) constrained optimization is based on the assumption that underlying uncertainty is driven by a probability distribution, that is, considered as a random vector. On the other hand, robust optimization deals with the situation in which uncertainty is given only by a membership of the uncertain factor to an explicitly defined set. We compare these two approaches with respect to the behavior of optimal values and optimal solution sets and discuss the drawbacks of each of two approaches on an illustrative example.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 10th International Scientific Conference INPROFORUM. Threatened Europe - Socio-Economic and Environmental Changes

ISBN

978-80-7394-607-4

ISSN

2336-6788

e-ISSN

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Počet stran výsledku

5

Strana od-do

187-191

Název nakladatele

Jihočeská univerzita v Českých Budějovicích, Ekonomická fakulta

Místo vydání

České Budějovice

Místo konání akce

České Budějovice

Datum konání akce

3. 11. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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