השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| תכנון ליניארי סטוכסטי× | סימולציית מונטה קרלו× | |
|---|---|---|
| תחום≠ | סימולציה | קבלת החלטות |
| משפחה≠ | Process / pipeline | MCDM |
| שנת המקור≠ | 1955 | 1949 |
| הוגה השיטה≠ | George B. Dantzig | Metropolis, N., Ulam, S. |
| סוג≠ | Stochastic optimization model | Robustness wrapper — Monte Carlo uncertainty propagation |
| מקור מכונן≠ | 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 ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| כינויים≠ | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | — |
| קשורות≠ | 5 | 0 |
| תקציר≠ | 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. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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