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| Programowanie stochastyczne z ograniczeniami całkowitoliczbowymi× | Programowanie stochastyczne liniowe× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1990s–2000s | 1955 |
| Twórca≠ | Birge, J. R.; Louveaux, F.; Sen, S. | George B. Dantzig |
| Typ | Stochastic optimization model | Stochastic optimization model |
| Źródło pierwotne≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 | Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176. link ↗ |
| Inne nazwy | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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. | Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world. |
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