A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization
Abstract
Sharper Approximation for Multistage Decisions Under Uncertainty In “A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization,” Thomä, Schiffer, and Wiesemann revisit piecewise affine decision rules, a widely used approximation for multistage stochastic programs. Building on the framework of Georghiou et al. (2015), the authors propose an algorithmic refinement that yields strictly better policies in stochastic settings while retaining tractability. Beyond stochastic programming, the framework naturally extends to multistage robust optimization and to modern data-driven models based on Wasserstein ambiguity sets. The paper shows how the resulting policies can be computed efficiently and provides numerical evidence demonstrating consistent performance improvements.
Fields of Study & Subjects
Fault Detection and Control Systems
Bayesian Modeling and Causal Inference
Multi-Criteria Decision Making
Publication details
- Journal / source
- Operations Research
- Publisher
- Institute for Operations Research and the Management Sciences (INFORMS)
- Volume
- 74
- Issue
- 4
- Pages
- 2075–2087
- Publication date
- 23 February 2026
- Language
- EN