Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management
This provides a practical framework for handling stochastic service durations in real-world routing applications, though it is an incremental improvement combining existing methods.
This paper tackled the problem of optimizing utility workforce routing by integrating machine learning forecasts of intervention durations into a stochastic vehicle routing problem, resulting in 20-25% improvements in operator utilization and completion rates compared to plans using default durations.
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.