Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Programación Entera Robusta× | Programación Lineal Robusta× | |
|---|---|---|
| Campo | Simulación | Simulación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2003 | 1999–2004 |
| Autor original≠ | Bertsimas, D. and Sim, M. | Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M. |
| Tipo≠ | Deterministic robust optimization with integer variables | Uncertainty-robust linear optimization |
| Fuente seminal≠ | Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71. DOI ↗ | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ |
| Alias | RIP, Robust IP, Robust Combinatorial Optimization, Integer Robust Optimization | RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | Robust Integer Programming (RIP) finds integer or binary solutions that remain feasible and near-optimal across all scenarios in a prescribed uncertainty set. Rather than assuming exact knowledge of data, RIP hedges against the worst-case realization of uncertain costs or constraint coefficients, delivering decisions that are guaranteed to perform well even when inputs deviate from their nominal values. | 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. |
| ScholarGateConjunto de datos ↗ |
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