发表机构
Clemson University（克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对有限支撑机会约束规划中Big-M重构松弛弱、扩展性差的问题，提出两类交叉割及混合分离策略，在集成生产-分销规划实例上显著减少计算时间。
AI 中文摘要
具有有限支撑的机会约束规划（CCPs）的标准Big-M混合整数规划重构通常产生较弱的线性松弛，并且随着场景数量的增加而扩展性变差。为了解决这一局限性，我们从问题特定的S-free集推导出两族交叉割，并开发了基于分解的分支切割框架。第一族利用场景需求函数的子模结构，而第二族使用概率覆盖。我们提出了混合分离策略，将经典的混合不等式和分位数割与所提出的交叉割相结合。在集成生产-分销规划实例上的计算实验表明，与纯方法相比，所提出的混合方法减少了计算时间。
英文摘要
Standard Big-M mixed-integer programming reformulations of chance-constrained programs (CCPs) with finite support often yield weak linear relaxations and scale poorly as the number of scenarios increases. To address this limitation, we derive two families of intersection cuts from problem-specific S-free sets and develop decomposition-based branch-and-cut frameworks. The first one exploits the submodular structure of the scenario requirement function, while the second one uses probability covers. We propose hybrid separation strategies that combine classical mixing inequalities and quantile cuts with the proposed intersection cuts. Computational experiments on integrated production-distribution planning instances show that the proposed hybrid methods reduce computational time compared to pure methods.
