Abstract
We introduce the stochastic linear programming (SLP) model classes, which will be considered in this paper, on the basis of a small-scale linear programming problem. The solutions for the various problem formulations are discussed in a comparative fashion. We point out the need for model and solution analysis. Subsequently, we outline the basic ideas of selected major algorithms for two classes of SLP problems: two-stage recourse problems and problems with chance constraints. Finally, we illustrate the computational behavior of two algorithms for large-scale SLP problems.
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Mayer, J. (2006). On the Numerical Solution of Stochastic Optimization Problems. In: Ceragioli, F., Dontchev, A., Futura, H., Marti, K., Pandolfi, L. (eds) System Modeling and Optimization. CSMO 2005. IFIP International Federation for Information Processing, vol 199. Springer, Boston, MA. https://doi.org/10.1007/0-387-33006-2_18
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DOI: https://doi.org/10.1007/0-387-33006-2_18
Publisher Name: Springer, Boston, MA
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