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

Authors: Tianming Zu

Addresses: Department of Management Engineering, Jilin Communications Polytechnic, Changchun, 130012, China

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.

Keywords: BP neural algorithm; adaptive; logistics enterprise; vehicle scheduling; least square method; grey relational analysis; travelling salesman problems; route optimisation.

DOI: 10.1504/IJDS.2023.131430

International Journal of Data Science, 2023 Vol.8 No.2, pp.152 - 168

Received: 21 Jul 2022
Accepted: 28 Sep 2022
Published online: 12 Jun 2023 *

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