Process / pipelineSimulation / optimization
随机整数规划 — 不确定性下离散决策的优化
随机整数规划（Stochastic Integer Programming, SIP）是一种优化框架，它将整数（离散）决策变量与不确定性的显式概率建模相结合。它旨在寻找最佳的“此时此地”决策，以最小化未来情景分布中的预期成本（或最大化预期收益），同时考虑到某些决策必须在不确定性解决之前做出。
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Method map
The neighbourhood of related methods — select a node to explore.
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来源
- Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4
- Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T. (2002). The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 12(2), 479-502. DOI: 10.1137/S1052623499363220 ↗
如何引用本页
ScholarGate. (2026, June 3). Stochastic Integer Programming (SIP). ScholarGate. https://scholargate.app/zh/simulation/stochastic-integer-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.
- 混合整数规划仿真↔ compare
- 鲁棒整数规划仿真↔ compare
- 随机动态规划仿真↔ compare
- 随机线性规划仿真↔ compare
- 随机混合整数规划仿真↔ compare
- 随机多目标优化仿真↔ compare