Robust Optimization using Machine Learning for Uncertainty Sets

TL;DR

This paper introduces a data-driven approach to robust optimization, learning uncertainty sets from complex past data using statistical learning theory to ensure probabilistic robustness guarantees.

Contribution

It presents a novel method for constructing uncertainty sets from data in robust optimization, with theoretical guarantees on robustness.

Findings

New data-driven uncertainty set design

Probabilistic robustness guarantees derived from statistical learning theory

Applicable to high-dimensional, complex data environments

Abstract

Our goal is to build robust optimization problems for making decisions based on complex data from the past. In robust optimization (RO) generally, the goal is to create a policy for decision-making that is robust to our uncertainty about the future. In particular, we want our policy to best handle the the worst possible situation that could arise, out of an uncertainty set of possible situations. Classically, the uncertainty set is simply chosen by the user, or it might be estimated in overly simplistic ways with strong assumptions; whereas in this work, we learn the uncertainty set from data collected in the past. The past data are drawn randomly from an (unknown) possibly complicated high-dimensional distribution. We propose a new uncertainty set design and show how tools from statistical learning theory can be employed to provide probabilistic guarantees on the robustness of the policy.