|edoc-Server der Humboldt-Universität zu Berlin|
Vikas Goel, Carnegie Mellon University|
Ignacio E. Grossmann, Carnegie Mellon University
|Title:||A class of stochastic programs with decision dependent uncertainty|
|Date of Acceptance:||02.10.2004|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-10059657)|
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|The standard approach to formulating stochastic programs is based on the assumption that the stochastic process is independent of the optimization decision. We address a class of problems where the optimization decisions influence the time of information discovery for a subset of the uncertain parameters. We extentd the standard modeling approach by presenting a disjunctive programming formulation that accommodates stochastic programs for this class of ploblems. A set of theoretical properties that lead to reduction in the size of the model is identified. A Lagrange duality based branch and bound algorithm is also presented.|
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