Application of data and algorithm to improve the efficiency of a single scheduling

By the use of machine learning and big data analysis techniques, we analyze and learn the data of past orders. According to priority principles such as the urgency of orders, vehicle model, route, loading condition, the least cost, and the shortest distance, we carry out an intelligent combination, allocation, and scheduling on sales orders to achieve the best allocation, the highest transportation efficiency and the best resource matching of orders. At present, the project has been implemented in Beijing Benz, and the efficiency of house B/L is improved by 75% compared with that under the traditional model.