A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization

• 成果类型: Article
• 署名作者: Thomae, Simon; Schiffer, Maximilian; Wiesemann, Wolfram
• 署名单位: RWTH Aachen University; Technical University of Munich; Technical University of Munich; Imperial College London
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2024.1344
• 发表日期: 2026
• 关键词: DECISION RULES; Stochastic Programming; robust optimization; approximation

摘要

Multistage decision making under uncertainty, where decisions are taken under sequentially revealing uncertain problem parameters, is often essential to faithfully model managerial problems. Given the significant computational challenges involved, these problems are typically solved approximately. This short note introduces an algorithmic framework that revisits a popular approximation scheme for multistage stochastic programs and improves on it to deliver superior policies in the stochastic setting, as well as extend its applicability to robust optimization and a contemporary Wasserstein-based data-driven setting. We demonstrate how the policies of our framework can be computed efficiently, and we present numerical experiments that highlight the benefits of our method.