Logistics Demand Forecasting and Scheduling Optimization Using K-Means Clustering and Deep Q-Network

With the rapid development of e-commerce and supply chain management, logistics demand forecasting and scheduling optimization have become key links in improving logistics efficiency and service quality. To meet this challenge, this paper proposes a logistics demand forecasting and scheduling optimization method that combines K-Means clustering with deep Q network (DQN). Cluster analysis is used to reduce data complexity, and deep reinforcement learning is used to achieve intelligent scheduling. First, the background, challenges and research motivation of logistics demand forecasting and scheduling optimization are introduced. Secondly, the research results in related fields are reviewed, and the shortcomings of current research are pointed out. Then, the K-Means clustering algorithm, deep $\mathbf{Q}$ network theory and specific models of logistics demand prediction and scheduling optimization are elaborated. Then, the performance of the proposed method is designed and tested. The experimental results show that the proposed method can significantly improve the accuracy of logistics demand prediction, up to 0.93, optimize scheduling strategies, reduce logistics costs, and improve customer satisfaction.