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Stochastic Linear Programming

Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world.

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Stochastic Linear Programming — Optimization under uncertainty with random parameters

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Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176. · URL
Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. · ISBN 9780387982175

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