{
  "annotation_id": "2e4a317d-2890-4897-813e-c242ef11a4f5",
  "date_created": "2026-02-17T05:53:12.056000Z",
  "date_modified": "2026-02-17T05:53:12.056000Z",
  "file_hash": "e6138f7c4294e81330c48aafc16fc124c40de246b09206db074c863894a8fcce",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.07514",
    "authors": [
      "Matteo Garbelli"
    ],
    "categories": [
      "math.OC",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management",
    "url": "https://arxiv.org/abs/2601.07514",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4e086547-425b-4fbc-8ff8-bfc68eeffd41",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}