Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Детерминированное целочисленное программирование× | Стохастическое целочисленное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1958 | 1955 |
| Автор метода≠ | Ralph E. Gomory | Dantzig, G. B.; Beale, E. M. L. |
| Тип≠ | Exact combinatorial optimization | Optimization under uncertainty with discrete decisions |
| Основополагающий источник≠ | Gomory, R. E. (1958). Outline of an algorithm for integer solutions to linear programs. Bulletin of the American Mathematical Society, 64(5), 275-278. DOI ↗ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 |
| Другие названия | DIP, Integer Programming, IP, Integer Linear Programming | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Deterministic Integer Programming (DIP) is a mathematical optimization approach that finds the best solution to problems where some or all decision variables must take integer values, given fully known (deterministic) objective and constraint data. It is the classical, non-stochastic form of integer programming, foundational to operations research and combinatorial optimization since the late 1950s. | 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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