A Framework for Inherently Interpretable Optimization Models
This addresses the issue of interpretability in optimization for practitioners, but it is incremental as it adapts existing interpretability methods from machine learning to optimization.
The paper tackles the problem of low acceptance of optimized solutions due to black-box optimization software by proposing a framework for inherently interpretable optimization models, using decision trees to provide easily interpretable rules, and finds that the costs of this interpretability can be very small in computational experiments.
With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world applications into the reach of optimizers. At the same time, solving optimization problems often turns out to be one of the smaller difficulties when putting solutions into practice. One major barrier is that the optimization software can be perceived as a black box, which may produce solutions of high quality, but can create completely different solutions when circumstances change leading to low acceptance of optimized solutions. Such issues of interpretability and explainability have seen significant attention in other areas, such as machine learning, but less so in optimization. In this paper we propose an optimization framework that inherently comes with an easily interpretable optimization rule, that explains under which circumstances certain solutions are chosen. Focusing on decision trees to represent interpretable optimization rules, we propose integer programming formulations as well as a heuristic method that ensure applicability of our approach even for large-scale problems. Computational experiments using random and real-world data indicate that the costs of inherent interpretability can be very small.