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SPEPS Preprint

Author(s): Thomas Heinze, University of Duisburg-Essen
Rüdiger Schultz, University of Duisburg-Essen
Title: A Branch-and-Bound Method for Multistage Stochastic Integer Programs with Risk Objectives
Date of Acceptance: 03.06.2007
Submission Date: 08.01.2007
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint: pdf (urn:nbn:de:kobv:11-10078083)
Keywords (eng): stochastic integer programming, mean-risk models, mixed-integer optimization, multistage models
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Abstract (eng):
We identify multistage stochastic integer programs with risk objectives where the related wait-and-see problems enjoy similar separability as in the risk neutral case. For models belonging to this class we present a solution method combining branch-and-bound with relaxation of nonanticipativity and constraint branching along nonanticipativity subspaces.
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