Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Стохастичне динамічне програмування× | Стохастичне лінійне програмування× | |
|---|---|---|
| Галузь | Імітаційне моделювання | Імітаційне моделювання |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1957 | 1955 |
| Автор методу≠ | Bellman, R.; formalized for stochastic settings by Puterman, M. L. | George B. Dantzig |
| Тип≠ | Sequential optimization under uncertainty | Stochastic optimization model |
| Основоположне джерело≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 | 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 ↗ |
| Інші назви | SDP, Markov Decision Process, MDP, Stochastic DP | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | 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 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. |
| ScholarGateНабір даних ↗ |
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