Online Resource Allocation with Convex-Set Machine-Learned Advice

Going Beyond Single-Point Demand Forecasts in Real-Time Allocation

Many companies use machine learning to forecast future demand when making real-time allocation decisions, such as deciding how many airline seats, hotel rooms, or advertising opportunities to reserve for different customer groups. Most existing approaches rely on a single demand prediction, or “point estimate.” In the paper “Online Resource Allocation with Convex-Set Machine-Learned Advice,” Negin Golrezaei, Patrick Jaillet, and Zijie Zhou develop a new framework that goes beyond point estimates by representing forecasts as a convex uncertainty set—a range of possible demand outcomes for both high-reward and low-reward customers. This richer form of machine-learned advice captures uncertainty, variability, and correlations in demand while also allowing realized demand to differ from the forecast. The authors develop adaptive online algorithms with provable theoretical guarantees, showing how to optimally balance performance when forecasts are accurate with robustness when forecasts are inaccurate or misleading. Numerical studies further demonstrate that the proposed approach outperforms methods based only on single-point predictions.