DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer
Programming for Multi-transfer Truck Freight Delivery
- URL: http://arxiv.org/abs/2103.03450v2
- Date: Thu, 25 May 2023 14:28:11 GMT
- Title: DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer
Programming for Multi-transfer Truck Freight Delivery
- Authors: Jiayu Chen, Abhishek K. Umrawal, Tian Lan, and Vaneet Aggarwal
- Abstract summary: DeepFreight is a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery.
The proposed system is highly scalable and ensures a 100% delivery success while maintaining low delivery-time and fuel consumption.
- Score: 38.04321619061474
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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