Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Programación Lineal Estocástica× | Simulación de Monte Carlo× | |
|---|---|---|
| Campo≠ | Simulación | Toma de decisiones |
| Familia≠ | Process / pipeline | MCDM |
| Año de origen≠ | 1955 | 1949 |
| Autor original≠ | George B. Dantzig | Metropolis, N., Ulam, S. |
| Tipo≠ | Stochastic optimization model | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | — |
| Relacionados≠ | 5 | 0 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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