Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Детерминированное линейное программирование× | Робастное линейное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1947 | 1999–2004 |
| Автор метода≠ | George B. Dantzig | Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M. |
| Тип≠ | Deterministic mathematical optimization | Uncertainty-robust linear optimization |
| Основополагающий источник≠ | Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ. ISBN: 9780691059136 | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ |
| Другие названия | Classical LP, Deterministic LP, DLP, Linear Optimization | RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP |
| Связанные | 5 | 5 |
| Сводка≠ | Deterministic Linear Programming (DLP) is the classical form of linear programming in which all objective function coefficients, constraint coefficients, and right-hand-side values are known with certainty. It finds the optimal allocation of resources to maximize or minimize a linear objective subject to linear constraints, providing an exact, reproducible solution under fixed, certain data. | Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited. |
| ScholarGateНабор данных ↗ |
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