Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Стохастичне лінійне програмування× | Стохастичне цільове програмування× | |
|---|---|---|
| Галузь | Імітаційне моделювання | Імітаційне моделювання |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1955 | 1968 |
| Автор методу≠ | George B. Dantzig | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| Тип≠ | Stochastic optimization model | Stochastic multi-goal optimization |
| Основоположне джерело≠ | 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 ↗ | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ |
| Інші назви | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | 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 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. |
| ScholarGateНабір даних ↗ |
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