Towards Robust Interpretable Surrogates for Optimization

TL;DR

This paper develops robust, interpretable decision tree surrogates for optimization that effectively handle uncertainty, aiming to improve practical acceptance and reliability of optimization solutions.

Contribution

It introduces models and solution methods for creating robust, interpretable decision tree surrogates that incorporate uncertainty in optimization problems.

Findings

Robust decision trees outperform non-robust ones under parameter perturbations.

Heuristic methods are effective for constructing interpretable, robust surrogates.

The proposed approaches are competitive with existing interpretable optimization frameworks.

Abstract

An important factor in the practical implementation of optimization models is the acceptance by the intended users. This is influenced among other factors by the interpretability of the solution process. Decision rules that meet this requirement can be generated using the framework for inherently interpretable optimization models. In practice, there is often uncertainty about the parameters of an optimization problem. An established way to deal with this challenge is the concept of robust optimization. The goal of our work is to combine both concepts: to create decision trees as surrogates for the optimization process that are more robust to perturbations and still inherently interpretable. For this purpose we present suitable models based on different variants to model uncertainty, and solution methods. Furthermore, the applicability of heuristic methods to perform this task is evaluated. Both approaches are compared with the existing framework for inherently interpretable optimization models.