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SPEPS Preprint

Author(s): 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
Submission Date: 05.06.2004
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint: pdf (urn:nbn:de:kobv:11-10059657)
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Abstract (eng):
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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