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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