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| Programowanie całkowitoliczbowe dla scenariuszy politycznych× | Programowanie stochastyczne z ograniczeniami całkowitoliczbowymi× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1950s–1960s (scenario extension: 1990s onwards) | 1955 |
| Twórca≠ | Operations research community (Dantzig, Gomory, and others) | Dantzig, G. B.; Beale, E. M. L. |
| Typ≠ | Discrete combinatorial optimization under scenario uncertainty | Optimization under uncertainty with discrete decisions |
| Źródło pierwotne≠ | Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402367 | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 |
| Inne nazwy | PSIP, scenario-based integer programming, policy-driven IP, scenario integer optimization | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming |
| Pokrewne≠ | 2 | 6 |
| Podsumowanie≠ | Policy Scenario Integer Programming (PSIP) solves an integer programming model — where some or all decision variables must take whole-number values — separately under each of several distinct policy scenarios, then compares objective values, feasibility, and solution structures to identify which policy environment leads to the best discrete allocation or assignment outcome. | 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. |
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