Predict-Then-Optimize
Overview
Predict-then-optimize studies pipelines that predict quantities such as costs, demand, or outcomes and feed those estimates into an optimization model to choose actions. Methods include decision-focused losses such as SPO+, differentiable and solver-free surrogates, bilevel or KKT reformulations, and robust or conformal approaches to uncertainty. They are used in settings including shortest paths, knapsack problems, portfolios, energy scheduling, and resource allocation. A central tension is that predictive accuracy may not reflect decision quality, while methods that improve decisions must also address computational scale, uncertainty, and fairness.
Latest papers
36 papers8 papers in the last four weeks, up 14% on the four weeks before. 0.0% of all new papers.