Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management

• URL: http://arxiv.org/abs/2601.07514v1
• Date: Mon, 12 Jan 2026 13:12:46 GMT
• Title: Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management
• Authors: Matteo Garbelli,
• Abstract summary: We exploit tree-based boosting gradient (XGBoost) trained on eight years of gas meter maintenance data to produce point predictions and uncertainty estimates.<n>Our results report improvements around 20-25% in operator completion rates compared with plans computed using default durations.
• Score: 0.913755431537592
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
  Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
  arXiv  Detail & Related papers  (2026-03-04T14:48:53Z)
• Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization [28.53710231018475]
  We propose an active acquisition algorithm that avoids explicit posterior inference while remaining uncertainty-aware through inverse curvature.<n>GOIMDA selects inputs by maximizing their expected influence on a user-specified goal functional.<n>We show theoretically that, for generalized linear models, GOIMDA approximates predictive-entropy minimization up to a correction term accounting for goal alignment and prediction bias.
  arXiv  Detail & Related papers  (2026-02-23T07:57:11Z)
• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• Adaptive Conformal Prediction Intervals Over Trajectory Ensembles [50.31074512684758]
  Future trajectories play an important role across domains such as autonomous driving, hurricane forecasting, and epidemic modeling.<n>We propose a unified framework based on conformal prediction that transforms sampled trajectories into calibrated prediction intervals with theoretical coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-18T21:14:07Z)
• Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling [1.124958340749622]
  This paper investigates the use of machine learning-based surrogate models to predict network-wide congestion caused by road renovations.<n>XGBoost significantly outperforms alternatives in a range of metrics, most strikingly Mean Absolute Percentage Error (MAPE)<n>This modeling approach has the potential to reduce the computational burden of large-scale traffic assignment problems in maintenance planning.
  arXiv  Detail & Related papers  (2025-06-06T09:59:05Z)
• Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data [0.0]
  We propose a deep-learning framework that blends real-world operational records with synthetically generated mechanical-breakdown scenarios.<n>We evaluate two architectures: an XGBoost regressor achieving a median absolute error (MedAE) of 14.3 per cent and a Long Short-Term Memory network with a MedAE of 15.1 per cent.
  arXiv  Detail & Related papers  (2025-06-04T12:12:56Z)
• Semiparametric Counterfactual Regression [2.356908851188234]
  We propose a doubly robust-style estimator for counterfactual regression within a generalizable framework.<n>Our approach uses incremental interventions to enhance adaptability while maintaining with standard methods.<n>Our analysis shows that the proposed estimators can achieve $sqrn$-consistency and normality for a broad class of problems.
  arXiv  Detail & Related papers  (2025-04-03T15:32:26Z)
• QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting [0.18416014644193066]
  We introduce QBSD, a live single-step forecasting approach tailored to optimize the trade-off between accuracy and computational complexity. QBSD has shown significant success with our real network RAN datasets of over several thousand cells. Results demonstrate that the proposed method excels in runtime efficiency compared to the leading algorithms available.
  arXiv  Detail & Related papers  (2023-06-09T15:59:27Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load [10.36283155920333]
  This work proposes a data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is used to extract health indicator in dynamic operating conditions. The proposed data-driven prognostics approach provides a competitive prognostics horizon with lower computational cost.
  arXiv  Detail & Related papers  (2023-02-03T12:52:49Z)
• Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes [70.80716221080118]
  The paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning. The OS-GPTD approach is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs. To alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme.
  arXiv  Detail & Related papers  (2021-12-01T23:15:09Z)
• Variance-Aware Off-Policy Evaluation with Linear Function Approximation [85.75516599931632]
  We study the off-policy evaluation problem in reinforcement learning with linear function approximation. We propose an algorithm, VA-OPE, which uses the estimated variance of the value function to reweight the Bellman residual in Fitted Q-Iteration.
  arXiv  Detail & Related papers  (2021-06-22T17:58:46Z)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.