方法对比
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| 随机目标规划× | 随机线性规划× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1968 | 1955 |
| 提出者≠ | Contini, B. (building on Charnes & Cooper's chance-constrained programming) | George B. Dantzig |
| 类型≠ | Stochastic multi-goal optimization | Stochastic optimization model |
| 开创性文献≠ | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ | 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 | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | 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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