Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Stochastische Lineaire Programmering× | Monte Carlo Simulatie× | |
|---|---|---|
| Vakgebied≠ | Simulatie | Besluitvorming |
| Familie≠ | Process / pipeline | MCDM |
| Jaar van ontstaan≠ | 1955 | 1949 |
| Grondlegger≠ | George B. Dantzig | Metropolis, N., Ulam, S. |
| Type≠ | Stochastic optimization model | Robustness wrapper — Monte Carlo uncertainty propagation |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen≠ | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | — |
| Verwant≠ | 5 | 0 |
| Samenvatting≠ | 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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