# A review of approaches to modeling applied vehicle routing problems

arXiv: https://arxiv.org/abs/2105.10950 · HTML: https://mlsanity.com/paper/2105.10950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 7 of 7)

- [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)
- [Learn to Design the Heuristics for Vehicle Routing Problem](https://mlsanity.com/paper/2002.08539/) (2020)
- [Type Inference for Static Compilation of JavaScript (Extended Version)](https://mlsanity.com/paper/1608.07261/) (2016)

## Built on by (most cited first, 1 of 1)

- [Optimizing delivery for quick commerce factoring qualitative assessment of generated routes](https://mlsanity.com/paper/2510.08671/) (2025)

## Abstract (arXiv)

Due to the practical importance of vehicle routing problems (VRP), there exists an ever-growing body of research in algorithms and (meta)heuristics for solving such problems. However, the diversity of VRP domains creates the separate problem of modeling such problems -- describing the domain entities (and, in particular, the planning decisions), the set of valid planning decisions, and the preferences between different plans. In this paper, we review the approaches for modeling vehicle routing problems. To make the comparison more straightforward, we formulate several criteria for evaluating modeling methods reflecting the practical requirements of the development of optimization algorithms for such problems. Finally, as a result of this comparison, we discuss several future research avenues in the field of modeling VRP domains.