  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 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.
