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| Programowanie stochastyczne z ograniczeniami całkowitoliczbowymi× | Programowanie stochastyczne z ograniczeniami całkowitoliczbowymi× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1955 | 1990s–2000s |
| Twórca≠ | Dantzig, G. B.; Beale, E. M. L. | Birge, J. R.; Louveaux, F.; Sen, S. |
| Typ≠ | Optimization under uncertainty with discrete decisions | Stochastic optimization model |
| Źródło pierwotne≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 |
| Inne nazwy | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP |
| Pokrewne≠ | 6 | 5 |
| 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. | Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario trees or expected-value objectives that hedge against uncertainty while respecting combinatorial constraints. |
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