# Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems

arXiv: https://arxiv.org/abs/2109.13983 · HTML: https://mlsanity.com/paper/2109.13983/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [Learning to Perform Local Rewriting for Combinatorial Optimization](https://mlsanity.com/paper/1810.00337/) (2018)
- [Learning Improvement Heuristics for Solving Routing Problems](https://mlsanity.com/paper/1912.05784/) (2019)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood](https://mlsanity.com/paper/2012.10384/) (2020)
- [Efficient Active Search for Combinatorial Optimization Problems](https://mlsanity.com/paper/2106.05126/) (2021)
- [Deep Policy Dynamic Programming for Vehicle Routing Problems](https://mlsanity.com/paper/2102.11756/) (2021)
- [Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking](https://mlsanity.com/paper/2005.11081/) (2020)

## Built on by (most cited first, 16 of 16)

- [Graph Neural Network Guided Local Search for the Traveling Salesperson Problem](https://mlsanity.com/paper/2110.05291/) (2021)
- [PyVRP: a high-performance VRP solver package](https://mlsanity.com/paper/2403.13795/) (2023)
- [Efficient Neural Neighborhood Search for Pickup and Delivery Problems](https://mlsanity.com/paper/2204.11399/) (2022)
- [Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives](https://mlsanity.com/paper/2406.00415/) (2024)
- [Learning to Solve Vehicle Routing Problems: A Survey](https://mlsanity.com/paper/2205.02453/) (2022)
- [Continuation Path Learning for Homotopy Optimization](https://mlsanity.com/paper/2307.12551/) (2023)
- [Neural Improvement Heuristics for Graph Combinatorial Optimization Problems](https://mlsanity.com/paper/2206.00383/) (2022)
- [A Neural Separation Algorithm for the Rounded Capacity Inequalities](https://mlsanity.com/paper/2306.17283/) (2023)
- [An Expandable Machine Learning-Optimization Framework to Sequential Decision-Making](https://mlsanity.com/paper/2311.06972/) (2023)
- [Learning-Enhanced Neighborhood Selection for the Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2403.08839/) (2024)
- [Feature-Guided Metaheuristic with Diversity Management for Solving the Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2407.20777/) (2024)
- [Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing](https://mlsanity.com/paper/2504.02383/) (2025)
- [End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW](https://mlsanity.com/paper/2512.01518/) (2025)
- [Multi-Agent Environments for Vehicle Routing Problems](https://mlsanity.com/paper/2411.14411/) (2024)
- [Hybrid Node-Destroyer Model with Large Neighborhood Search for Solving the Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2508.08659/) (2025)
- [Edge-Selector Model Applied for Local Search Neighborhood for Solving Vehicle Routing Problems](https://mlsanity.com/paper/2508.14071/) (2025)

## Abstract (arXiv)

Despite the extensive research efforts and the remarkable results obtained on Vehicle Routing Problems (VRP) by using algorithms proposed by the Machine Learning community that are partially or entirely based on data-driven analysis, most of these approaches are still seldom employed by the Operations Research (OR) community. Among the possible causes, we believe, the different approach to the computational evaluation of the proposed methods may play a major role. With the current work, we want to highlight a number of challenges (and possible ways to handle them) arising during the computational studies of heuristic approaches to VRPs that, if appropriately addressed, may produce a computational study having the characteristics of those presented in OR papers, thus hopefully promoting the collaboration between the two communities.