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| Стохастично линейно програмиране× | Стохастично целево програмиране× | |
|---|---|---|
| Област | Симулационно моделиране | Симулационно моделиране |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1955 | 1968 |
| Създател≠ | George B. Dantzig | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| Тип≠ | Stochastic optimization model | Stochastic multi-goal optimization |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| Свързани≠ | 5 | 6 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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