mlsanityData to 7 Oct 2026

Analytics and Machine Learning in Vehicle Routing Research

arXiv 2102.10012 ↗First posted 19 Feb 2021cs.LG cs.AI math.OCcited by 22

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Abstract (arXiv)

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered in several traditional research fields with very different, sometimes confusing, terminologies. This paper presents a first, comprehensive review of hybrid methods that combine analytical techniques with ML tools in addressing VRP problems. Specifically, we review the emerging research streams on ML-assisted VRP modelling and ML-assisted VRP optimisation. We conclude that ML can be beneficial in enhancing VRP modelling, and improving the performance of algorithms for both online and offline VRP optimisations. Finally, challenges and future opportunities of VRP research are discussed.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 34 of its 216 references that are in the map. First posted on arXiv 19 Feb 2021.

Builds on

34 of 216 references.

References · 34

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22 more
  1. 2019Learning Improvement Heuristics for Solving Routing Problemscited by 162
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  3. 2019Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problemcited by 117
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 2; 2024: 5; 2025: 2; 2026: 6.

22 papers.

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  1. 2021Deep Policy Dynamic Programming for Vehicle Routing Problemscited by 94
  2. 2024Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalizationcited by 49
  3. 2021An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problemcited by 22
  4. 2021Improving Generalization of Deep Reinforcement Learning-based TSP Solverscited by 8
  5. 2021Generalization in Deep RL for TSP Problems via Equivariance and Local Searchcited by 8
  6. 2022Attention, Filling in The Gaps for Generalization in Routing Problemscited by 7
  7. 2024Prompt Learning for Generalized Vehicle Routingcited by 6
  8. 2025Learning for routing: A guided review of recent developments and future directionscited by 6
  9. 2026Survey on Neural Routing Solverscited by 4
  10. 2023Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristiccited by 3
  11. 2025Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problemscited by 3
  12. 2021Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!cited by 2
10 more
  1. 2024Learning-Enhanced Neighborhood Selection for the Vehicle Routing Problem with Time Windowscited by 2
  2. 2024GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problemscited by 2
  3. 2026Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptionscited by 1
  4. 2022Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problemcited by 0
  5. 2023Digital Twin Applications in Urban Logistics: An Overviewcited by 0
  6. 2024Pattern based learning and optimisation through pricing for bin packing problemcited by 0
  7. 2026Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Managementcited by 0
  8. 2026Constraints Matrix Diffusion based Generative Neural Solver for Vehicle Routing Problemscited by 0
  9. 2026Adaptive decision-making for stochastic service network designcited by 0
  10. 2026Preference-Agile Multi-Objective Optimization for Real-time Vehicle Dispatchingcited by 0