Feature-Based Interpretable Surrogates for Optimization

TL;DR

This paper introduces a novel approach for creating interpretable surrogate models for optimization using feature-based rules, enhancing trust and flexibility in practical decision-making.

Contribution

It proposes a new framework using general optimization rules for interpretability, with exact and heuristic methods for rule discovery, improving solution quality over existing surrogates.

Findings

Improved solution quality with feature-based rules

Demonstrated effectiveness on synthetic and real data

Enhanced interpretability and decision-maker flexibility

Abstract

For optimization models to be used in practice, it is crucial that users trust the results. A key factor in this aspect is the interpretability of the solution process. A previous framework for inherently interpretable optimization models used decision trees to map instances to solutions of the underlying optimization model. Based on this work, we investigate how we can use more general optimization rules to further increase interpretability and, at the same time, give more freedom to the decision-maker. The proposed rules do not map to a concrete solution but to a set of solutions characterized by common features. To find such optimization rules, we present an exact methodology using mixed-integer programming formulations as well as heuristics. We also outline the challenges and opportunities that these methods present. In particular, we demonstrate the improvement in solution quality that our approach offers compared to existing interpretable surrogates for optimization, and we discuss the relationship between interpretability and performance. These findings are supported by experiments using both synthetic and real-world data.