Affiliations: [a] School of Logistics and Transportation and Tourism, Jiangsu Vocational College of Finance and Economics, Huaian, Jiangsu, China | [b] School of Economics and Management, Sichuan Vocational College of Chemical Technology, Luzhou, Sichuan, China | [c] School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
Correspondence:
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Corresponding author: Bin Liu, School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu 61000, Sichuan, China. E-mail: [email protected].
Abstract: Logistics distribution is an indispensable part of the modern economy and is crucial for ensuring the efficient operation of the supply chain. With the continuous progress of technology, the application of intelligent scheduling systems in the field of logistics distribution is becoming increasingly widespread. Reinforcement learning, as one of the hot technologies in the field of artificial intelligence, is gradually receiving attention in its application in intelligent scheduling. Reinforcement learning can continuously learn and predict the same thing to enhance memory, while intelligent scheduling requires continuous prediction and optimization of logistics distribution paths. In response to the current problems of slow logistics distribution efficiency and low customer satisfaction, this article analyzed the application of intelligent scheduling in logistics distribution from the aspects of basic data maintenance, basic data review, intelligent scheduling, scheduling result review, distribution information management, and vehicle tracking. By using reinforcement learning, the traffic network weight in logistics distribution was studied to improve logistics distribution efficiency and customer satisfaction. This article analyzed the efficiency of logistics distribution, vehicle tracking accuracy, vehicle scheduling ability, and logistics distribution costs under different logistics distributions. The results showed that the logistics distribution under the integration of reinforcement learning and intelligent scheduling reduced 12.047 km compared to traditional distribution paths, and its distribution cost decreased by 129.718 yuan compared to traditional logistics distribution costs. The efficiency of logistics distribution that integrates reinforcement learning and intelligent scheduling has significantly improved, with optimized distribution costs and paths. It also has a positive effect on improving the utilization rate of logistics distribution vehicles.
