Dynamic Clustering and Simulation of Retail E-commerce Order Based on Sorting Robot

WANG Chen, YIN Jing, WANG Hong-chun

Packaging Engineering ›› 2020 ›› Issue (3) : 170-175.

PDF(394 KB)
PDF(394 KB)
Packaging Engineering ›› 2020 ›› Issue (3) : 170-175. DOI: 10.19554/j.cnki.1001-3563.2020.03.026

Dynamic Clustering and Simulation of Retail E-commerce Order Based on Sorting Robot

  • WANG Chen1, YIN Jing1, WANG Hong-chun2
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Abstract

The work aims to improve the sorting efficiency and dynamic response capability of orders for e-commerce logistics distribution centers. Based on analyzing such factors as the characteristics of retail e-commerce orders with multiple varieties, small batches and high aging, movement and load constraints of sorting robots were taken into consideration; a rolling window scheduling strategy and a high-dimensional sparse dynamic clustering algorithm were proposed, and a simulation experiment model of a large-scale e-commerce distribution center was established for data comparison analysis. The simulation experiment was carried out on 500 orders of an e-commerce enterprise in rush hours. Compared with the fixed batch sorting strategy, the optimized sorting strategy reduced the average moving distance of the robot by 66.9% and the sorting time by 23.9%. The conclusion is that the high-dimensional sparse dynamic clustering strategy effectively improves the sorting efficiency, reduces the sorting cost, and the algorithm is more open and flexible. It is of great significance for the e-commerce enterprise to reduce costs and increase efficiency of logistics business.

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WANG Chen, YIN Jing, WANG Hong-chun. Dynamic Clustering and Simulation of Retail E-commerce Order Based on Sorting Robot[J]. Packaging Engineering. 2020(3): 170-175 https://doi.org/10.19554/j.cnki.1001-3563.2020.03.026
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