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Deviation measures in stochastic programming with mixed-integer recourse

by Maerkert, Andreas

Abstract (Summary)
Stochastic programming offers a way to treat uncertainty in decision problems. In particular, it allows the minimization of risk. We consider mean-risk models involving deviation measures, as for instance the standard deviation and the semideviation, and discuss these risk measures in the framework of stochastic dominance as well as in the framework of coherent risk measures. We derive statements concerning the structure and the stability of the resulting optimization problems whereby we emphasize on models including integrality requirements on some decision variables. Then we propose decomposition algorithms for the mean-risk models under consideration and present numerical results for two stochastic programming applications.
Bibliographical Information:

Advisor:Prof. Dr. RĂ¼diger Schultz; Prof. Dr. Maarten H. van der Vlerk

School:Universität Duisburg-Essen, Standort Essen

School Location:Germany

Source Type:Master's Thesis

Keywords:mathematik universitaet duisburg essen

ISBN:

Date of Publication:06/09/2004

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