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Stochastic Integer Programming

Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved.

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Stochastic Integer Programming (SIP)

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Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. · ISBN 978-1-4614-0237-4
Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T. (2002). The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 12(2), 479-502. · DOI 10.1137/S1052623499363220

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