# Learning-Enhanced Neighborhood Selection for the Vehicle Routing Problem with Time Windows

arXiv: https://arxiv.org/abs/2403.08839 · HTML: https://mlsanity.com/paper/2403.08839/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [Learning Improvement Heuristics for Solving Routing Problems](https://mlsanity.com/paper/1912.05784/) (2019)
- [Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/1911.09539/) (2019)
- [Learning to Delegate for Large-scale Vehicle Routing](https://mlsanity.com/paper/2107.04139/) (2021)
- [Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs](https://mlsanity.com/paper/2107.10201/) (2021)
- [Analytics and Machine Learning in Vehicle Routing Research](https://mlsanity.com/paper/2102.10012/) (2021)
- [Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems](https://mlsanity.com/paper/2109.13983/) (2021)
- [A generalized formulation for vehicle routing problems](https://mlsanity.com/paper/1606.01935/) (2016)

## Built on by (most cited first, 2 of 2)

- [Learning for routing: A guided review of recent developments and future directions](https://mlsanity.com/paper/2507.00218/) (2025)
- [HyP-ASO: A Hybrid Policy-based Adaptive Search Optimization Framework for Large-Scale Integer Linear Programs](https://mlsanity.com/paper/2509.15828/) (2025)

## Abstract (arXiv)

Large Neighborhood Search (LNS) is a universal approach that is broadly applicable and has proven to be highly efficient in practice for solving optimization problems. We propose to integrate machine learning (ML) into LNS to assist in deciding which parts of the solution should be destroyed and repaired in each iteration of LNS. We refer to our new approach as Learning-Enhanced Neighborhood Selection (LENS for short). Our approach is universally applicable, i.e., it can be applied to any LNS algorithm to amplify the workings of the destroy algorithm. In this paper, we demonstrate the potential of LENS on the fundamental Vehicle Routing Problem with Time Windows (VRPTW). We implemented an LNS algorithm for VRPTW and collected data on generated novel training instances derived from well-known, extensively utilized benchmark datasets. We trained our LENS approach with this data and compared the experimental results of our approach with two benchmark algorithms: a random neighborhood selection method to show that LENS learns to make informed choices and an oracle neighborhood selection method to demonstrate the potential of our LENS approach. With LENS, we obtain results that significantly improve the quality of the solutions.