The Vehicle Routing Problem (VRP) is a widely studied combinatorial optimization problem and has been applied to various practical problems. While the explainability for VRP is significant for improving the reliability and interactivity in practical VRP applications, it remains unexplored. In this paper, we propose RouteExplainer, a post-hoc explanation framework that explains the influence of each edge in a generated route. Our framework realizes this by rethinking a route as the sequence of actions and extending counterfactual explanations based on the action influence model to VRP. To enhance the explanation, we additionally propose an edge classifier that infers the intentions of each edge, a loss function to train the edge classifier, and explanation-text generation by Large Language Models (LLMs). We quantitatively evaluate our edge classifier on four different VRPs. The results demonstrate its rapid computation while maintaining reasonable accuracy, thereby highlighting its potential for deployment in practical applications. Moreover, on the subject of a tourist route, we qualitatively evaluate explanations generated by our framework. This evaluation not only validates our framework but also shows the synergy between explanation frameworks and LLMs. See https://ntt-dkiku.github.io/xai-vrp for our code, datasets, models, and demo.

翻译：车辆路径问题（VRP）是一个被广泛研究的组合优化问题，并已应用于各类实际问题。尽管VRP的可解释性对于提升实际VRP应用中的可靠性和交互性至关重要，但这一领域至今仍未被探索。本文提出RouteExplainer，一种事后解释框架，用于解释生成路径中每条边的影响力。我们的框架通过将路径重新视为动作序列，并基于动作影响模型将反事实解释扩展到VRP来实现这一目标。为增强解释效果，我们额外提出了一种边分类器来推断每条边的意图、用于训练该边分类器的损失函数，以及基于大语言模型（LLM）的解释文本生成方法。我们在四种不同的VRP上对边分类器进行了定量评估，结果表明其计算速度快且能保持合理精度，从而凸显了其在实际应用中部署的潜力。此外，我们以旅游路线为例，对框架生成的解释进行了定性评估。该评估不仅验证了我们的框架，还展示了解释框架与LLM之间的协同效应。代码、数据集、模型及演示请参见https://ntt-dkiku.github.io/xai-vrp。