The intention of this article is to solve the disadvantages of the current logistics model and promote the healthy development of modern cross-border e-commerce (CBEC) Logistics. First, this paper expounds on and compares the traditional and CBEC logistics models. Then, the CBEC logistics system is constructed and adjusted according to system construction requirements. Further, two key subsystems are designed: the logistics object distribution subsystem and risk detection subsystem, based on the deep learning backpropagation neural network (BPNN) algorithm. The relevant parameters of the object distribution subsystem are calculated and sent to the risk detection subsystem model and tested. It is concluded that the sorting completion rate before 18:00 can reach 95.2%, indicating that the proposed CBEC logistic system can meet the needs of CBEC logistics enterprises. Logistics risk detection’s expected and actual outputs can fit 99%, indicating a tiny deviation. The research has certain reference significance for clarifying the logistics system and service mode of CBEC.
This study aims to solve the problem of unbalanced regional economic development in Jiangsu and deeply excavates the relevant theories of regional logistics and the unbalanced spatial situation. The classification of regional logistics and spatial disequilibrium and the dimensions involved are studied. At present, the development status of Jiangsu logistics has locked the main reason for the unbalanced situation of the regional logistics space in Jiangsu. The basic principles, application scope, and methods of Bagging and Boosting algorithms are deeply studied. Then, the Jiangsu logistics space nonequilibrium situation assessment model is established based on the Bagging and Boosting algorithms, and the input data is sorted and summarized. Finally, the results are obtained through experiments evaluating the model and analyzed and summarized. The core issues, the factors affecting development, and the uneven development situation have been comprehensively assessed. The conclusion drawn is as follows: the imbalance of the logistics productivity of various regions in Jiangsu will inevitably lead to the imbalance of the regional logistics space in Jiangsu, which affects the coordinated and healthy development of the regional logistics in Jiangsu. These conclusions are significant to the inherent causes and effects of regional logistics spatial disequilibrium and can promote the coordination, synergy, and common development of Jiangsu logistics regions.
The purpose of this article is to solve the problem that the accuracy of logistics distribution path planning is affected by the lack of data in the process of traditional logistics distribution planning and management. This exploration innovatively applies an effective data addition algorithm expectation-maximization (EM) algorithm to the intelligent logistics distribution system to improve logistics distribution’s overall efficiency and management quality. First, the concept of intelligent logistics and the composition and main functions of the intelligent logistics system are introduced. Then, the core idea of the EM algorithm and its applications in intelligent logistics are described. The logistics distribution of a chain company is taken as an example. Finally, the advantages and disadvantages of the intelligent logistics system based on the EM algorithm are compared with those of the traditional intelligent logistics systems based on variable neighborhood search (VNS), Tabu search (TS), and ant colony optimization (ACO). The performance test results show that the EM algorithm’s optimal solution times are 7 times. Its convergence speed is slightly lower than that of the ACO, but there is no obvious difference. The intelligent logistics distribution system based on the EM algorithm has faster order processing speed and higher efficiency in the actual case application. The average processing time of each order is 1.78 min, which is 0.237 min less than that of VNS and only 0.022 min more than that of ACO. It reveals that the intelligent logistics distribution system based on the EM algorithm is more efficient. The study provides a new idea for the efficient distribution of enterprise logistics.
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