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Vol. 242, Issue 3, March 2020, pp. 6-11




An Experimental Model of Deep Learning Logistics Distribution
Based on Internet of Things

1, Jixiang LU and 2 *Xiao HAN

1 School of Mechanical Engineering , Northwestern Polytechnical University, Xi'an 710072, China
2 Hamburg University of Technology, Hamburg, 21107, Germany
1 Tel.: +86 13319288957 (CHN)

E-mail: lujixiang@nwpu.edu.cn


Received: 25 February 2019 /Accepted: 25 March 2019 /Published: 31 March 2020

Digital Sensors and Sensor Sysstems


Abstract: Intelligent logistics is not only an important industry in the world, but also an application and research hotspot of artificial intelligence. It is based on the Internet of things (IoT). Improving the user experience is the key of intelligent logistics, which needs to be optimized according to the change of user needs. The design of IoT based intelligent scheduling is one of the key technologies to improve user experience. First, we use the deep learning network to learn the urban environment, and guide the logistics company to wait for the vehicle to contact the receiving and delivering users in the shortest time. Different from the existing scheduling designs which are based on reinforcement learning, the high-dimensional logistics environment is considered in this paper during establishment of reinforcement learning scheduling model. These factors have a great impact on improving the efficiency of delivery and receiving as well as the benefit of couriers.


Keywords: Internet of things, optimization design, intelligent logistics, intelligent scheduling, deep learning, reinforcement learning


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