An Adaptive Partition-based Level Decomposition for Solving Two-stage Stochastic Programs with Fixed Recourse
An Adaptive Partition-based Level Decomposition for Solving Two-stage Stochastic Programs with Fixed Recourse
复制标题
求解具有固定追索权的两阶段随机规划的基于自适应划分的层次分解
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Yongjia Song
中科院分区：
文献类型：
--
作者：
W. Oliveira;Yongjia Song
将基于自适应划分的方法与水平分解相结合，对求解两阶段随机线性规划的几种策略进行了计算研究。基于划分的公式是通过根据情景划分聚集变量和约束而获得的原始随机程序的松弛。分区精化由在某些第一级解上计算的最优第二级对偶向量来指导。所提出的方法依赖于具有按需精度的层次分解来动态调整分区，直到找到最优解。在一大组测试问题上的数值实验表明，所提出的方法是有效的。
We present a computational study of several strategies to solve two-stage stochastic linear programs by integrating the adaptive partition-based approach with level decomposition. A partition-based formulation is a relaxation of the original stochastic program, obtained by aggregating variables and constraints according to a scenario partition. Partition refinements are guided by the optimal second-stage dual vectors computed at certain first-stage solutions. The proposed approaches rely on the level decomposition with on-demand accuracy to dynamically adjust partitions until an optimal solution is found. Numerical experiments on a large set of test problems including instances with up to one hundred thousand scenarios show the effectiveness of the proposed approaches.
在两阶段随机规划中应用按需精度预言 - 计算研究
DOI：
10.1016/j.ejor.2014.05.010
发表时间：
2014
期刊：
Eur. J. Oper. Res.
影响因子：
--
作者：
Christian Wolf;Csaba Fabian;Achim Koberstein;Leena Suhl
通讯作者：
Leena Suhl