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贝叶斯整数规划——概率先验引导的组合优化

贝叶斯整数规划（BIP）将贝叶斯概率推理与整数规划相结合，以解决不确定性下的组合优化问题。它不将参数视为固定值，而是对不确定的系数进行先验信念编码，并用观测数据更新这些信念，从而产生一个由后验分布引导的整数可行解搜索。该方法广泛应用于调度、资源分配和供应链规划等数据不完整或存在噪声的领域。

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贝叶斯整数规划
贝叶斯线性规划贝叶斯混合整数规划贝叶斯多目标优化混合整数规划鲁棒整数规划随机整数规划

来源

  1. Baptiste, P., Lassagne, I., & Nuijten, W. (2001). Bayesian reasoning in mixed integer programming. European Journal of Operational Research, 130(2), 293–313. link ↗
  2. Bayesian optimization. Wikipedia. link ↗

如何引用本页

ScholarGate. (2026, June 3). Bayesian Integer Programming — Probabilistic Prior-Guided Combinatorial Optimization. ScholarGate. https://scholargate.app/zh/simulation/bayesian-integer-programming

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ScholarGate — Bayesian Integer Programming (Bayesian Integer Programming — Probabilistic Prior-Guided Combinatorial Optimization). 于 2026-06-15 检索自 https://scholargate.app/zh/simulation/bayesian-integer-programming · 数据集： https://doi.org/10.5281/zenodo.20539026