用可解释的决策树替代优化模型，提升对参数扰动的鲁棒性。
Towards Robust Interpretable Surrogates for Optimization
- 构建基于不确定建模变体的可解释代理模型
- 在保持可解释性的同时，增强对参数扰动的鲁棒性
- 适合关注模型可信度与稳定性的工业优化场景
优化模型在实际应用中的接受度受解法可解释性的重要影响。可通过内在可解释优化模型框架生成满足此要求的决策规则。实践中常面临优化问题参数不确定的问题，传统应对方法是鲁棒优化。本文目标是结合二者：构建对参数扰动更鲁棒且仍具内在可解释性的决策树代理模型。为此，提出基于不同不确定性建模方式及求解方法的合适模型，并评估启发式方法的适用性。两种方法均与现有内在可解释优化框架进行对比。
原文摘要 · Abstract (English)
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.
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