# DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery

arXiv: https://arxiv.org/abs/2103.03450 · HTML: https://mlsanity.com/paper/2103.03450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [MAVEN: Multi-Agent Variational Exploration](https://mlsanity.com/paper/1910.07483/) (2019)
- [Boltzmann Exploration Done Right](https://mlsanity.com/paper/1705.10257/) (2017)
- [DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03882/) (2019)
- [MOVI: A Model-Free Approach to Dynamic Fleet Management](https://mlsanity.com/paper/1804.04758/) (2018)
- [A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.14002/) (2019)
- [Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10897/) (2020)
- [Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2008.10713/) (2020)

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

- [Opportunities of Reinforcement Learning in South Africa's Just Transition](https://mlsanity.com/paper/2411.15145/) (2024)

## Abstract (arXiv)

With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collaborative components: truck-dispatch and package-matching. Specifically, a deep multi-agent reinforcement learning framework called QMIX is leveraged to learn a dispatch policy, with which we can obtain the multi-step joint vehicle dispatch decisions for the fleet with respect to the delivery requests. Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks. Also, DeepFreight is integrated with a Mixed-Integer Linear Programming optimizer for further optimization. The evaluation results show that the proposed system is highly scalable and ensures a 100% delivery success while maintaining low delivery-time and fuel consumption. The codes are available at https://github.com/LucasCJYSDL/DeepFreight.