A note on piecewise affine decision rules for robust, stochastic, and data-driven optimization
File(s)
Accepted version
Author(s)
Thomä, Simon
Schiffer, Maximilian
Wiesemann, Wolfram
Type
Journal Article
Abstract
Multi-stage 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 multi-stage stochastic programs by Georghiou et al. (2015) and improves upon 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.
Date Issued
2026-02-23
Date Acceptance
2026-01-16
Citation
Operations Research, 2026
ISSN
0030-364X
Publisher
Institute for Operations Research and Management Sciences
Journal / Book Title
Operations Research
Copyright Statement
Copyright © 2026, INFORMS. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published online
Date Publish Online
2026-02-23