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

Operations Research, vol. 74, pp. 2075–2087

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.

Authors 3

1. Simon Thomä corresponding Aachen Chair of Operations Management

   RWTH Aachen University · Technical University of Munich

   Affiliation as printed

   Chair of Operations Management, RWTH Aachen, 52072 Aachen, Germany; and School of Management, Technical University of Munich, 80333 Munich, Germany

2. Maximilian Schiffer

   Technical University of Munich

   Affiliation as printed

   School of Management, Technical University of Munich, 80333 Munich, Germany; and Munich Data Science Institute, Technical University of Munich, 80333 Munich, Germany

3. Wolfram Wiesemann

   Imperial College London

   Affiliation as printed

   Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom