Process / pipelineSimulation / optimization
随机线性规划——不确定性下的随机参数优化
随机线性规划（SLP）将经典线性规划扩展到模型参数（成本、需求、资源可用性）不确定并被建模为随机变量的场景。通过在场景的概率分布上优化期望成本，SLP 生成的决策在各种可能未来中保持可行且接近最优，而不是针对单一假设的世界状态。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
- Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 9780387982175
如何引用本页
ScholarGate. (2026, June 3). Stochastic Linear Programming — Optimization under uncertainty with random parameters. ScholarGate. https://scholargate.app/zh/simulation/stochastic-linear-programming
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →