مقایسهٔ روشها
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| برنامهریزی هدف بیزی× | برنامهریزی هدف قوی× | |
|---|---|---|
| حوزه | شبیهسازی | شبیهسازی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1990s | 1961 (GP); 1990s (robust extension) |
| پدیدآور≠ | Rios Insua, D. and colleagues | Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework) |
| نوع≠ | Multi-objective optimization under uncertainty | Mathematical programming under uncertainty |
| منبع بنیادین≠ | Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814 | Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041 |
| نامهای دیگر | BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization | RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming |
| مرتبط≠ | 6 | 5 |
| خلاصه≠ | Bayesian Goal Programming (BGP) integrates Bayesian statistical inference with classic goal programming to handle uncertainty in targets and parameters. Instead of treating goal thresholds as fixed constants, BGP encodes them as probability distributions, updates beliefs using observed data, and then solves the resulting probabilistic optimization problem to find solutions that satisfy multiple aspirational goals under uncertainty. | 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. |
| ScholarGateمجموعهداده ↗ |
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