Application of Artificial Intelligence Algorithms in B2C E-Commerce Logistics Distribution Path Optimization

With the rapid development of e-commerce, the efficiency of logistics distribution has become an important indicator that determines the core competitiveness of enterprises. This article applied artificial intelligence (AI) algorithms to B2C (Business-to-Consumer) e-commerce platforms and optimized them to improve delivery efficiency and reduce costs. This article intended to use improved genetic algorithms and other methods to construct a multi-objective logistics distribution path optimization model. Through simulation experiments, the effectiveness and stability of the proposed method were verified under different operating conditions (such as vehicle quantity, order quantity, weather conditions, etc.). The total delivery time decreased from 6.5 hours for 5 vehicles to 5.2 hours for 20 vehicles, indicating that increasing the number of vehicles during transportation can effectively improve transportation efficiency and shorten transportation time. The research results of this article provide a scientific and feasible logistics path optimization method for e-commerce enterprises to improve their service quality and customer satisfaction.