Intelligent Optimization of Logistics Paths Based on Improved Artificial Bee Colony Algorithm

Abstract

With the rapid development of e-commerce and logistics industry, logistics path optimization has gradually become a key bottleneck restricting enterprises from improving service quality and reducing operating cost. Therefore, the study introduces the elite bee colony strategy and proposes an intelligent optimization model for logistics paths on the basis of an improved artificial bee colony algorithm. The performance test results demonstrated that the model had good convergence speed and accuracy in both unimodal and multi-modal function tests, achieving the optimal mean and standard deviation. The area enclosed by the model precision-recall curve and the coordinate axis was 0.96, and the F1 value was 97.7%. In the simulation test, the model planned the delivery path for 20 customer points with a total path length of 5221.43km, a total delivery cost of 83908.38 yuan, a vehicle loading rate of 97.4%, and a solution time of 2.8s. The research results indicate that the logistics path intelligent optimization method has good comprehensive performance. It can effectively reduce logistics transportation costs, which has important guiding significance and application value for actual logistics operations. This model not only provides an efficient path optimization tool for logistics enterprises, but also offers new ideas and methods for logistics path optimization research.

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