Compară metode

	Programare Stocastică cu Numere Întregi	Programare Liniară Stocastică
Domeniu	Simulare	Simulare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1955	1955
Autorul original	Dantzig, G. B.; Beale, E. M. L.	George B. Dantzig
Tip	Optimization under uncertainty with discrete decisions	Stochastic optimization model
Sursa seminală	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4	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.
Denumiri alternative	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
Înrudite	6	5
Rezumat	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.	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.