Abstract
50 years ago, stochastic programming was introduced to deal with uncertain values of coefficients which were observed in applications of mathematical programming. These uncertainties were modeled as random and the assumption of complete knowledge of the probability distribution of random parameters became a standard. Hence, there is a new type of uncertainty concerning the probability distribution. Using a hypothetical, ad hoc distribution may lead to bad, costly decisions. Besides of a subsequent output analysis it pays to include the existing, possibly limited information into the model, cf. the minimax approach which will be the main item of this presentation. It applies to cases when the probability distribution is only known to belong to a specified class of probability distributions and one wishes to hedge against the least favorable distribution. The minimax approach has been developed for special types of stochastic programs and special choices of the class of probability distributions and there are recent results aiming at algorithmic solution of minimax problems and on stability properties of minimax solutions.
BibTeX  Entry
@InProceedings{dupacov:DSP:2005:45,
author = {Jitka Dupacov{\'a}},
title = {Uncertainties in stochastic programming models: The minimax approach},
booktitle = {Algorithms for Optimization with Incomplete Information},
year = {2005},
editor = {Susanne Albers and Rolf H. M{\"o}hring and Georg Ch. Pflug and R{\"u}diger Schultz},
number = {05031},
series = {Dagstuhl Seminar Proceedings},
ISSN = {18624405},
publisher = {Internationales Begegnungs und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2005/45},
annote = {Keywords: stochastic programming models , minimax approach}
}
Keywords: 

stochastic programming models , minimax approach 
Seminar: 

05031  Algorithms for Optimization with Incomplete Information 
Issue Date: 

2005 
Date of publication: 

27.05.2005 