2005-11-15Buch DOI: 10.18452/2693
On Stochastic Integer Programming under Probabilistic Constraints
We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of p-efficient points of a probability distribution is used to derive various equivalent problem formulations. Next we modify the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive new lower and upper bounds for the optimal value of probabilistically constrained stochastic programming problems with integer random variables. We also show how limited information about the distribution can be used to construct such bounds.
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