Generalized adaptive partition-based method for two-stage stochastic linear programs with fixed recourse

• 成果类型: Article
• 署名作者: Ramirez-Pico, Cristian; Moreno, Eduardo
• 署名单位: Universidad Adolfo Ibanez
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-020-01609-8
• 发表日期: 2022
• 页码: 755-774
• 关键词: decomposition method; algorithm; optimization; aggregation; bounds; RISK

摘要

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.