Efficient Logistics Path Optimization and Scheduling Using Deep Reinforcement Learning and Convolutional Neural Networks

Abstract

With the rapid development of e-commerce and online shopping, the logistics industry is facing unprecedented challenges. Traditional logistics path - planning methods, such as SPA, HA, GA, etc., struggle to cope with the complex and ever-changing logistics environment. To address this issue, this study proposes an innovative model that combines Deep reinforcement learning (DRL) with a Convolutional neural network (CNN) to achieve efficient logistics path optimization. In this research, a detailed analysis and pre-processing of the public datasets, the City Logistics Dataset (CLDS) and the Traffic Status Dataset (TSDS), were carried out to construct a model capable of effectively handling diverse logistics environments. Six baseline methods, namely the classic shortest path algorithm (SPA), heuristic algorithm (HA), genetic algorithm (GA), rule-based method (RBM), traditional deep reinforcement learning method (TDRM), and the most advanced deep learning method (ADLM), were selected for comparison. The experimental results indicate that the proposed model performs excellently across various environments. For instance, in suburban areas, it achieves a path length of 180 kilometers, a completion time of 120 minutes, a punctuality rate of 92%, and a dispatch success rate of 95%. In urban settings, the path length is 200 kilometers, the completion time is 150 minutes, the punctuality rate is 90%, and the dispatch success rate is 93%. On highways, it reaches a path length of 170 kilometers, a completion time of 110 minutes, a punctuality rate of 93%, and a dispatch success rate of 95%. Compared with the baseline methods, the model shows significant improvements in key metrics such as path length, completion time, punctuality, and dispatch success rate. Additionally, it outperforms them in terms of computation time and robustness scores, demonstrating great potential for practical applications.