DBNTFPO: ANN-Based Approach for Logistics Distribution Optimization

Abstract

This research aims to improve the optimization ability of traditional algorithms under complex urban road conditions. Henceforth, in this paper, the logistics distribution path optimization problem model is established. Each part of the model is introduced in detail, the weight update of the ant algorithm is introduced, the problem of unreasonable road parameters set by the ant algorithm is solved, and the Deep Belief Network Traffic Forecast Path Optimization (DBNTFPO) algorithm is proposed. The related applications of deep learning technology are analyzed, and the relationship between deep learning technology and real-time distribution vehicle routing problem is discussed. Finally, the challenges brought by the real-time logistics distribution path optimization problem to deep learning are introduced. Finally, the effectiveness and feasibility of the algorithm in the actual logistics distribution are demonstrated through an example analysis.