Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастическое целевое программирование× | Стохастическое целочисленное 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) | Dantzig, G. B.; Beale, E. M. L. |
| Тип≠ | Stochastic multi-goal optimization | Optimization under uncertainty with discrete decisions |
| Основополагающий источник≠ | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 |
| Другие названия | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming |
| Связанные | 6 | 6 |
| 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 Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved. |
| ScholarGateНабор данных ↗ |
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