Сравнение методов

	Стохастическое динамическое программирование	Стохастическое смешанно-целочисленное программирование
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1957	1990s–2000s
Автор метода	Bellman, R.; formalized for stochastic settings by Puterman, M. L.	Birge, J. R.; Louveaux, F.; Sen, S.
Тип	Sequential optimization under uncertainty	Stochastic optimization model
Основополагающий источник	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175
Другие названия	SDP, Markov Decision Process, MDP, Stochastic DP	SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP
Связанные	6	5
Сводка	Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.	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.