We present a method to solve two-stage stochastic linear programming problems with fixed recourse when the uncertainty space can have either discrete or continuous distributions. Given a partition of the uncertainty space, the method is addressed to solve a discrete problem with one scenario for each element of the partition (subregions of the uncertainty space). Fixing first-stage variables, we formulate a second-stage subproblem for each element, and exploiting information from the dual of these problems, we provide conditions that the partition must satisfy to obtain an optimal solution. These conditions provide guidance on how to refine the partition, iteratively approaching an optimal solution. The results from computational experiments show how the method automatically refines the partition of the uncertainty space in the regions of interest for the problem. Our algorithm is a generalization of the adaptive partition-based method presented by Song and Luedtke for discrete distributions, extending its applicability to more general cases.

当不确定空间可以具有离散或连续分布时，我们提出了一种具有固定追索权的两阶段随机线性规划问题的解决方法。在给定不确定性空间的分区的情况下，解决该方法以针对分区的每个元素（不确定性空间的子区域）使用一个场景解决离散问题。固定第一阶段变量后，我们为每个元素制定第二阶段子问题，并利用这些问题对偶中的信息，为获得最佳解决方案提供了分区必须满足的条件。这些条件为如何逐步优化最佳解决方案提供了指导。计算实验的结果表明，该方法如何在问题的关注区域自动优化不确定空间的划分。我们的算法是Song和Luedtke针对离散分布提出的基于自适应分区的方法的推广，将其适用性扩展到更一般的情况。