Сравнение методов

	Программирование целочисленных сценариев политики	Стохастическое целочисленное программирование
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1950s–1960s (scenario extension: 1990s onwards)	1955
Автор метода	Operations research community (Dantzig, Gomory, and others)	Dantzig, G. B.; Beale, E. M. L.
Тип	Discrete combinatorial optimization under scenario uncertainty	Optimization under uncertainty with discrete decisions
Основополагающий источник	Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402367	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4
Другие названия	PSIP, scenario-based integer programming, policy-driven IP, scenario integer optimization	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming
Связанные	2	6
Сводка	Policy Scenario Integer Programming (PSIP) solves an integer programming model — where some or all decision variables must take whole-number values — separately under each of several distinct policy scenarios, then compares objective values, feasibility, and solution structures to identify which policy environment leads to the best discrete allocation or assignment outcome.	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.