Related papers: Feature-Based Interpretable Surrogates for Optimization

Feature-Based Interpretable Surrogates for Optimization

URL: http://arxiv.org/abs/2409.01869v2
Date: Fri, 27 Sep 2024 13:04:55 GMT
Title: Feature-Based Interpretable Surrogates for Optimization
Authors: Marc Goerigk, Michael Hartisch, Sebastian Merten, Kartikey Sharma,
Abstract summary: In this work, we investigate how we can use more general optimization rules to increase interpretability. The proposed rules do not map to a concrete solution but to a set of solutions characterized by common features. In particular, we demonstrate the improvement in solution quality that our approach offers compared to existing interpretable surrogates for optimization.
Score: 0.8437187555622164
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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