DeepFreight: A Model-free Deep-reinforcement-learning-based Algorithm for Multi-transfer Freight Delivery

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 shows that the proposed system is highly scalable and ensures a 100% delivery success while maintaining low delivery-time and fuel consumption.

Authors

• Jiayu Chen 0006
• Abhishek Kumar Umrawal
• Tian Lan 0001
• Vaneet Aggarwal

Keywords

• Multi-agent Planning And Learning
• Applications That Involve A Combination Of Learning With Planning Or Scheduling
• Reinforcement Learning Using Planning (model-based
• Bayesian
• Deep
• Etc.)
• Planning Applied To Automating Machine Learning Systems