Titlebook: Risk Management in Stochastic Integer Programming; With Application to Frederike Neise Book 2008 Vieweg+Teubner Verlag | Springer Fachmedi

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978-3-8348-0547-8Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2008

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Introduction, with and determining his decision’s consequences. Though substantially influenced by uncertainty, his decision should be optimal in some sense with respect to the possible future scenarios. If we assume the uncertain data to be given in form of random variables, such a situation can be described by a stochastic optimization problem.

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Stochastic Dominance Constraints induced by Mixed-Integer Linear Recourse,tional meanrisk approach to this problem, requires to select the random variable with respect to statistical parameters reflecting mean and/or risk, like the expectation, the random variable’s excess over some preselected ruin level, or the variable’s quantiles. In all cases, a “best” random variabl

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