# Collaborative vehicle routing: a survey

arXiv: https://arxiv.org/abs/1706.05254 · HTML: https://mlsanity.com/paper/1706.05254/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach](https://mlsanity.com/paper/2310.17485/) (2023)
- [Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing](https://mlsanity.com/paper/2310.17458/) (2023)
- [Using Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2307.12136/) (2023)
- [Individually Rational Collaborative Vehicle Routing through Give-And-Take Exchanges](https://mlsanity.com/paper/2308.16501/) (2023)

## Abstract (arXiv)

In horizontal collaborations, carriers form coalitions in order to perform parts of their logistics operations jointly. By exchanging transportation requests among each other, they can operate more efficiently and in a more sustainable way. Collaborative vehicle routing has been extensively discussed in the literature. We identify three major streams of research: (i) centralized collaborative planning, (ii) decentralized planning without auctions, and (ii) auction-based decentralized planning. For each of them we give a structured overview on the state of knowledge and discuss future research directions.