Robust Actionable Prescriptive Analytics

We propose a new robust actionable prescriptive analytics framework that leverages past data and side information to minimize a risk-based objective function under distributional ambiguity. Our framework aims to find a policy that directly transforms the side information into implementable decisions. Specifically, we focus on developing actionable response policies that offer the benefits of interpretability and implementability. To address the potential issue of overfitting to empirical data, we adopt a data-driven robust satisficing approach that effectively handles uncertainty. We tackle the computational challenge for linear optimization models with recourse by developing a new safe tractable approximation for robust constraints, accommodating bilinear uncertainty and general norm-based uncertainty sets. Additionally, we introduce a biaffine recourse adaptation to enhance the quality of the approximation. Furthermore, we present a localized robust satisficing model that efficiently solves combinatorial optimization problems with tree-based static policies. Finally, we demonstrate the practical application of our framework through a simulation case study on risk-minimizing portfolio optimization using past returns as side information. We also provide a simulation case study on how the framework can be applied to obtain an interpretable policy for allocating taxis to different demand regions in response to weather information.

Funding: The research of L. Chen was supported by the Emerging Scholar Research Fellowships, University of Sydney Business School. The research of M. Sim and L. Zhao was supported by the Ministry of Education, Singapore under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. The research of X. Zhang was supported by the Anhui Provincial Natural Science Foundation [Grant 2408085QG222], the Fundamental Research Funds for the Central Universities [Grant BJ2040160100], and the National Natural Science Foundation of China [Grant 72025201]. The research of M. Zhou was supported by the National Natural Science Foundation of China [Grants 72301075 and 72293564/72293560].

Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2023.0300.