Abstract
In high-density residential areas, intelligent express delivery is often used to solve a large number of express parcels that need to be delivered in a short time. It effectively improves the efficiency of parcel delivery by means of information technology. However, with the continuous development of e-commerce, the logistics demand of the last kilometer delivery is constantly changing, and the problems of intelligent parcel cabinets are also exposed. For example, the express cabinet for parcel delivery is far away from the consignee, and when there are many parcels to be picked up, the consignee takes multiple express routes circuitously, and the inability to pick up all express shipments at one time causes the consignee to pick up the parcels in time and occupy the express cabinet for a long time, which affects the delivery of parcels. The article is clustered based on two main factors: the volume of express delivery and the walking distance to the delivery point. Take the area with a large logistics delivery volume as the center point. Based on the adaptive K-means algorithm, the best combination of package sorting is constructed so that multiple packages of the same address are stored in the same area of the smart express cabinet as much as possible. After determining the best possible delivery location of express parcels, a dynamic optimization model of the parcel delivery location is constructed based on the deep neural network (DNN) algorithm. When the logistics demand fluctuates sharply, it can still effectively allocate massive express parcels to the optimal delivery Click in. In this paper, an empirical study is conducted with the Bei**g Mining Community as an example. The results show that the improved DNN algorithm can effectively improve the efficiency of parcel delivery based on smart express cabinets.
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Acknowledgement
Fund Project: Key project of Bei**g Social Science Foundation (19yja001); China University of mining and Technology (Bei**g) Yueqi young scholars project (2602021rc17); Special fund for graduate students of basic scientific research business expenses of Central Universities (2021yjsgl02); Project supported by the special fund for Social Sciences of basic scientific research business expenses of Central Universities (2021 skgl01); National undergraduate scientific research topic selection training program (c202105030).
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Yang, Y., Wang, Y., Zhang, J. (2022). An Improved DNN Algorithm in Sorting Optimization of Intelligent Parcel Cabinets. In: Sun, X., Zhang, X., **a, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1587. Springer, Cham. https://doi.org/10.1007/978-3-031-06761-7_36
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