Simulation of Logistics Frequent Path Data Mining Based on Statistical Density

Sharp increases and rapid development followed the effects of a novel coronavirus outbreak on online sales and the real economy. The e-commerce mode on the Internet has attracted much attention, and users' purchases on the Internet has never been done before. However, among the many express companies, as the ones closest to consumers, they can still provide high-quality products in the face of huge market demand. Urban terminal logistics refers to the purpose of express services to meet the needs of terminal customers under the requirements of logistics centralization and customer diversification. However, the geographical distribution of logistics services in China is comprehensive, and customers' requirements are also complex. Practical problems in logistics enterprises in China significantly restrict the quality of logistics services. The final kilometer of distribution is composed of many links, and it is a very cumbersome enlace; it contains the determination of distribution scope, loading goods, arrangement of distribution sequence, arrangement of vehicles or personnel scheduling, and planning of distribution routes. A Genetic Algorithm (GA) with local search method fusion is proposed for fast logistics data modeling and mining simulation analysis. Practical examples and literature data prove the method's accuracy.

@article{Hou2023,
  title     = {Simulation of Logistics Frequent Path Data Mining Based on Statistical Density},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Fengju Hou},
  doi       = {10.14569/IJACSA.2023.0140845},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140845}
}