Abstract
We consider mathematical programs with probabilistic constraints in which the random variables are discrete. In general, the feasible region associated with such problems is nonconvex. We use methods of disjunctive programming to approximate the convex hull of the feasible region. For a particular disjunctive set implied by the probabilistic constraint, we characterize the set of all valid inequalities as well as the facets of the convex hull of the given disjunctive set. These may be used within relaxation methods, especially for combinatorial optimization problems.
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© 1992 International Federation for Information Processing
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Sen, S. (1992). Relaxations for probabilistically constrained programs with discrete random variables. In: Davisson, L.D., et al. System Modelling and Optimization. Lecture Notes in Control and Information Sciences, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0113328
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DOI: https://doi.org/10.1007/BFb0113328
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55577-3
Online ISBN: 978-3-540-47220-9
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