Сравнение методов

	Стохастическое линейное программирование	Стохастическое смешанно-целочисленное программирование
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1955	1990s–2000s
Автор метода	George B. Dantzig	Birge, J. R.; Louveaux, F.; Sen, S.
Тип	Stochastic optimization model	Stochastic optimization model
Основополагающий источник	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.	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175
Другие названия	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP	SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP
Связанные	5	5
Сводка	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.	Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario trees or expected-value objectives that hedge against uncertainty while respecting combinatorial constraints.