Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Стохастичне цільове програмування× | Стохастичне лінійне програмування× | |
|---|---|---|
| Галузь | Імітаційне моделювання | Імітаційне моделювання |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1968 | 1955 |
| Автор методу≠ | Contini, B. (building on Charnes & Cooper's chance-constrained programming) | George B. Dantzig |
| Тип≠ | Stochastic multi-goal optimization | Stochastic optimization model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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