方法对比
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| 随机情景分析× | 随机线性规划× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1955–1980s | 1955 |
| 提出者≠ | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition | George B. Dantzig |
| 类型≠ | Probabilistic scenario enumeration and evaluation | Stochastic optimization model |
| 开创性文献≠ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 | Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176. link ↗ |
| 别名 | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| 相关≠ | 4 | 5 |
| 摘要≠ | Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is. | Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world. |
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