Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Stochastické lineární programování× | Stochastické programování s cíli× | |
|---|---|---|
| Obor | Simulace | Simulace |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1955 | 1968 |
| Tvůrce≠ | George B. Dantzig | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| Typ≠ | Stochastic optimization model | Stochastic multi-goal optimization |
| Původní zdroj≠ | 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 ↗ |
| Další názvy | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| Příbuzné≠ | 5 | 6 |
| Shrnutí≠ | 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. |
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