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| Програмно планиране на цели в сценарии за политики× | Устойчиво целево програмиране× | |
|---|---|---|
| Област | Симулационно моделиране | Симулационно моделиране |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1961 (goal programming); policy scenario application 1980s–present | 1961 (GP); 1990s (robust extension) |
| Създател≠ | Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature | Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework) |
| Тип≠ | Optimization under multiple conflicting goals across policy scenarios | Mathematical programming under uncertainty |
| Основополагащ източник | Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471153405 | Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041 |
| Други названия | PSGP, Policy GP, Scenario-based Goal Programming, Multi-scenario Goal Programming | RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming |
| Свързани | 5 | 5 |
| Резюме≠ | Policy Scenario Goal Programming (PSGP) integrates goal programming optimization with policy scenario analysis to evaluate how well competing policy objectives can be achieved under distinct future conditions. Decision-makers define multiple goals and several plausible policy scenarios, then solve a goal programming model for each scenario to identify which policy strategies best satisfy priority targets across the full scenario space. | 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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