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| Robuste lineare Programmierung× | Stochastische Lineare Programmierung× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1999–2004 | 1955 |
| Urheber≠ | Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M. | George B. Dantzig |
| Typ≠ | Uncertainty-robust linear optimization | Stochastic optimization model |
| Wegweisende Quelle≠ | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ | 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 ↗ |
| Aliasnamen | RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited. | 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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