Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912818279743488 |
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| author | Garbelli, Matteo |
| author_facet | Garbelli, Matteo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07514 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management Garbelli, Matteo Optimization and Control Artificial Intelligence 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. |
| title | Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management |
| topic | Optimization and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2601.07514 |