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| Programowanie stochastyczne z ograniczeniami całkowitoliczbowymi× | Programowanie całkowitoliczbowe odporne× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1955 | 2003 |
| Twórca≠ | Dantzig, G. B.; Beale, E. M. L. | Bertsimas, D. and Sim, M. |
| Typ≠ | Optimization under uncertainty with discrete decisions | Deterministic robust optimization with integer variables |
| Źródło pierwotne≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 | Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71. DOI ↗ |
| Inne nazwy | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming | RIP, Robust IP, Robust Combinatorial Optimization, Integer Robust Optimization |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | 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. | Robust Integer Programming (RIP) finds integer or binary solutions that remain feasible and near-optimal across all scenarios in a prescribed uncertainty set. Rather than assuming exact knowledge of data, RIP hedges against the worst-case realization of uncertain costs or constraint coefficients, delivering decisions that are guaranteed to perform well even when inputs deviate from their nominal values. |
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