In this section
蔼补谤迟颈肠濒别调罢丑辞尘盲:2026:10.1287/辞辫谤别.2024.1344,
author = {Thom盲, S and Schiffer, M and Wiesemann, W},
doi = {10.1287/opre.2024.1344},
journal = {Operations Research},
title = {A note on piecewise affine decision rules for robust, stochastic, and data-driven optimization},
url = {http://dx.doi.org/10.1287/opre.2024.1344},
year = {2026}
}
TY - JOUR
AB - 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.
AU - Thom盲,S
AU - Schiffer,M
AU - Wiesemann,W
DO - 10.1287/opre.2024.1344
PY - 2026///
SN - 0030-364X
TI - A note on piecewise affine decision rules for robust, stochastic, and data-driven optimization
T2 - Operations Research
UR - http://dx.doi.org/10.1287/opre.2024.1344
ER -