方法对比
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| 政策情景目标规划× | 随机目标规划× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1961 (goal programming); policy scenario application 1980s–present | 1968 |
| 提出者≠ | Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| 类型≠ | Optimization under multiple conflicting goals across policy scenarios | Stochastic multi-goal optimization |
| 开创性文献≠ | Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471153405 | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ |
| 别名 | PSGP, Policy GP, Scenario-based Goal Programming, Multi-scenario Goal Programming | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable. |
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