On deviation measures in stochastic integer programming

Andreas Märkert; Rüdiger Schultz

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SPEPS Preprint

Author(s):	Andreas Märkert, University of Duisburg-Essen Rüdiger Schultz, University of Duisburg-Essen
Title:	On deviation measures in stochastic integer programming
Date of Acceptance:	13.09.2004
Submission Date:	17.05.2004
Series Title:	Stochastic Programming E-Print Series (SPEPS)
Editors:	Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint:	pdf (urn:nbn:de:kobv:11-10059639)
Keywords (eng):	Stochastic programming, mean-risk models, mixed-integer optimization
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Abstract (eng):

We propose extensions of traditional expectation-based stochastic integer programs to mean-risk models. Risk is measured by expected deviations of suitable random variables from their means or from preselected targets. We derive structural properties of the resulting stochastic programs and present first algorithmic ideas to achieve problem decomposition.

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