Process / pipelineSimulation / optimization
随机混合整数规划 — 在不确定性下具有离散和连续决策的优化
随机混合整数规划 (Stochastic Mixed-Integer Programming, SMIP) 是一种优化框架，用于在成本、需求、产能等关键参数不确定并被建模为概率分布或一系列场景时，找到二元、整数和连续决策的最佳组合。它通过嵌入场景树或预期值目标来对冲不确定性，同时遵守组合约束，从而扩展了经典混合整数规划 (MIP)。
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来源
- Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175
- Sen, S., & Higle, J. L. (2005). The C3 theorem and a D2 algorithm for large scale stochastic mixed-integer programming: Set convexification. Mathematical Programming, 104(1), 1–20. DOI: 10.1007/s10107-004-0566-z ↗
如何引用本页
ScholarGate. (2026, June 3). Stochastic Mixed-Integer Programming (SMIP). ScholarGate. https://scholargate.app/zh/simulation/stochastic-mixed-integer-programming
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