Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

We review the operations research/management science literature on data‐driven methods
in retail operations. This line of work has grown rapidly in recent years, thanks to the …

Contextual optimization, also known as predict-then-optimize or prescriptive analytics,
considers an optimization problem with the presence of covariates (context or side …

The predict-then-optimize framework is fundamental in many practical settings: predict the
unknown parameters of an optimization problem, and then solve the problem using the …

In this paper, we study a novel approach for data-driven decision-making under uncertainty
in the presence of contextual information. Specifically, we solve this problem from a …

Incorporating side observations in decision making can reduce uncertainty and boost
performance, but it also requires that we tackle a potentially complex predictive relationship …

We study policy optimization for the feature-based newsvendor, which seeks an end-to-end
policy that renders an explicit mapping from features to ordering decisions. Most existing …

In this paper, we propose a novel approach for data-driven decision-making under
uncertainty in the presence of contextual information. Given a finite collection of …

This paper introduces the general philosophy of the Operational Data Analytics (ODA)
framework for data‐based decision modeling. The fundamental development of this …

We develop a tractable and flexible data-driven approach for incorporating side information
into multi-stage stochastic programming. The proposed framework uses predictive machine …