用特征规则替代具体解，让优化结果更可解释且灵活。
Feature-Based Interpretable Surrogates for Optimization
- 用特征集合定义优化规则，而非直接输出具体解。
- 在真实与合成数据上均提升解的质量表现。
- 适合需要透明决策过程的工程与管理场景。
为使优化模型在实践中获得用户信任，解决方案过程的可解释性至关重要。现有框架通过决策树将实例映射到优化模型的解，具备内在可解释性。本文在此基础上，探索使用更通用的优化规则以增强可解释性，并赋予决策者更多自由度。所提规则不直接映射到单一解，而是指向具有共同特征的解集。为此，提出基于混合整数规划的精确方法及启发式算法来发现此类规则，并分析其面临的挑战与机遇。实验表明，相比现有可解释代理模型，本方法在解质量上有所提升，同时探讨了可解释性与性能之间的权衡关系。研究基于合成数据和真实世界数据验证了有效性。
原文摘要 · Abstract (English)
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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