This paper investigates the integration of machine learning forecasts of intervention durations into a stochastic variant of the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). In particular, we exploit tree-based gradient boosting (XGBoost) trained on eight years of gas meter maintenance data to produce point predictions and uncertainty estimates, which then drive a multi-objective evolutionary optimization routine. The methodology addresses uncertainty through sub-Gaussian concentration bounds for route-level risk buffers and explicitly accounts for competing operational KPIs through a multi-objective formulation. Empirical analysis of prediction residuals validates the sub-Gaussian assumption underlying the risk model. From an empirical point of view, our results report improvements around 20-25\% in operator utilization and completion rates compared with plans computed using default durations. The integration of uncertainty quantification and risk-aware optimization provides a practical framework for handling stochastic service durations in real-world routing applications.

翻译：本文研究将机器学习预测的干预时长集成至带时间窗的容量限制车辆路径问题（CVRPTW）的随机变体中。具体而言，我们利用基于树的梯度提升算法（XGBoost）对八年燃气表维护数据进行训练，以生成点预测及不确定性估计，进而驱动多目标进化优化流程。该方法通过路径层级风险缓冲的子高斯集中界处理不确定性，并通过多目标建模明确考虑相互竞争的关键运营指标。对预测残差的实证分析验证了风险模型所基于的子高斯假设。从实证角度看，相较于使用默认时长计算的规划方案，我们的方法在操作员利用率和任务完成率方面实现了约20-25%的提升。不确定性量化与风险感知优化的结合为实际路径规划应用中处理随机服务时长提供了实用框架。