2004-10-02Buch DOI: 10.18452/2959
A class of stochastic programs with decision dependent uncertainty
Grossmann, Ignacio E.
Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
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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