Research on Real-Time Scheduling Optimization Algorithm of Logistics Big Data Based on Intelligent Algorithm

This paper proposes a real-time scheduling optimization algorithm based on big data of logistics distribution, aiming to improve the response speed and accuracy of material distribution through the design of intelligent scheduling system. The algorithm model comprehensively considers the dynamic changes of material supply chain, real-time traffic conditions of distribution routes and real-time updates of customer demand. By integrating advanced data analysis technology and machine learning algorithm, the system can monitor the indicators in the logistics distribution process in real time and dynamically adjust the scheduling plan accordingly. In addition, the algorithm also introduces a predictive model to predict potential delivery delays and make scheduling decisions in advance. In order to verify the effectiveness of the algorithm, detailed simulation experiments are carried out in this paper. The experimental results show that compared with the traditional scheduling method, the proposed algorithm shows significant advantages in reducing delivery time, reducing transportation cost and improving customer satisfaction. Especially in peak hours and complex road conditions, the optimization effect of the algorithm is more obvious, which effectively alleviates the pressure of logistics distribution.