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Application of adaptive back propagation neural network algorithm in vehicle scheduling of logistics enterprises

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  • Tianming Zu

Abstract

With the rapid development of modern logistics, customers have higher and higher requirements for order delivery. With the increasing logistics pressure, the logistics vehicle scheduling problem (VRP) has become the focus of the industry to ensure the timeliness and smoothness of logistics. Based on this, a vehicle scheduling model based on self-adaptation back propagation (SABP) is constructed. The results show that the prediction accuracy rate of the model established in the research is 96.5%, which is much higher than the prediction accuracy rate of the traditional support vector machine (SVM) model and the traditional BP neural network model. The SABP model can reach the expected accuracy after 208 iterations, and the number of iterations is much lower than the other two models. The experiment shows that the model can accurately predict the shortest path and complete the distribution with the lowest cost.

Suggested Citation

  • Tianming Zu, 2023. "Application of adaptive back propagation neural network algorithm in vehicle scheduling of logistics enterprises," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 8(2), pages 152-168.
    • Handle: RePEc:ids:ijdsci:v:8:y:2023:i:2:p:152-168
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