Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Programare Liniară Robustă× | Programare prin Obiective Robustă× | |
|---|---|---|
| Domeniu | Simulare | Simulare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1999–2004 | 1961 (GP); 1990s (robust extension) |
| Autorul original≠ | Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M. | Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework) |
| Tip≠ | Uncertainty-robust linear optimization | Mathematical programming under uncertainty |
| Sursa seminală≠ | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ | Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041 |
| Denumiri alternative | RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP | RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming |
| Înrudite | 5 | 5 |
| Rezumat≠ | 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. | Robust Goal Programming (RGP) extends classical goal programming to handle uncertain or ambiguous model parameters. Instead of minimizing deviations from crisp targets, it seeks solutions that remain feasible and near-optimal across a range of plausible scenarios or uncertain data realizations. RGP is particularly valuable in planning problems where goals are aspirational and input data carries inherent variability or estimation error. |
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