Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастическое линейное программирование× | Стохастическое динамическое программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1955 | 1957 |
| Автор метода≠ | George B. Dantzig | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Тип≠ | Stochastic optimization model | Sequential optimization under uncertainty |
| 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. link ↗ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Другие названия | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | SDP, Markov Decision Process, MDP, Stochastic DP |
| Связанные≠ | 5 | 6 |
| 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 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. |
| ScholarGateНабор данных ↗ |
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