Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастическое целевое программирование× | Стохастическое линейное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1968 | 1955 |
| Автор метода≠ | Contini, B. (building on Charnes & Cooper's chance-constrained programming) | George B. Dantzig |
| Тип≠ | Stochastic multi-goal optimization | Stochastic optimization model |
| Основополагающий источник≠ | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ | 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 ↗ |
| Другие названия | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable. | 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. |
| ScholarGateНабор данных ↗ |
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