Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Programación Estocástica Entera Mixta× | Simulación de Monte Carlo× | |
|---|---|---|
| Campo≠ | Simulación | Toma de decisiones |
| Familia≠ | Process / pipeline | MCDM |
| Año de origen≠ | 1990s–2000s | 1949 |
| Autor original≠ | Birge, J. R.; Louveaux, F.; Sen, S. | Metropolis, N., Ulam, S. |
| Tipo≠ | Stochastic optimization model | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fuente seminal≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alias≠ | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP | — |
| Relacionados≠ | 5 | 0 |
| Resumen≠ | 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. | 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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