用机器学习预测维修时间，优化电力工人调度效率。
Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management
- 用XGBoost模型预测维修时长及不确定性，输入优化算法。
- 实测工人利用率和完成率提升20%-25%。
- 适合需要应对时间不确定性的实际运维调度场景。
本文研究将机器学习对干预时长的预测融入带时间窗的容量限制车辆路径问题（CVRPTW）的随机变体中。利用八年燃气表维护数据训练基于树的梯度提升模型（XGBoost），生成点预测与不确定性估计，并驱动多目标进化优化流程。通过路径级别的风险缓冲区采用次高斯集中界来处理不确定性，同时以多目标形式显式考虑竞争性运营指标。对预测残差的实证分析验证了风险模型所依赖的次高斯假设。实证结果表明，相比使用默认时长规划，本方法使操作员利用率和完成率提升约20%-25%。不确定性量化与风险感知优化的结合，为现实世界中的随机服务时长提供了实用解决方案。
原文摘要 · Abstract (English)
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.
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