A Distribution Center Vehicle Cargo Matching System Based On Clustering And Improved Ant Colony Search

With the continuous expansion and complexity of logistics business,how to quickly and accurately complete vehicle cargo matching has become one of the key issues of concern in the logistics industry.However,the traditional logistics industry faces problems such as resource waste and untimely information updates.Therefore,it is necessary to further standardize the freight process in order to provide higher quality services to consumers.At present,the matching of vehicles and goods in distribution centers mainly relies on platform delivery,which is a great waste of transportation capacity.Especially in the delivery process,the delivery process is mainly manual loading and unloading.The existing mode wastes a lot of vehicle transportation resources,and the time cost and efficiency of the vehicle loading and unloading process are relatively high.At the same time,the problem of information asymmetry is also more prominent for delivery drivers,cargo owners,and distribution centers.This article is dedicated to solving the problems faced by logistics distribution centers in the transportation process,such as loading and unloading time,distribution vehicle driving time,and vehicle loading rate.In order to achieve the optimal vehicle cargo matching scheme,we constructed a vehicle cargo matching model targeting the operating cost of the distribution center,and comprehensively considered practical factors such as loading and unloading time,vehicle driving time,and vehicle loading rate.In this model,we first cluster the delivery points of goods,and then improve the probability formula of ant colony algorithm’s ant selection of the next node to consider the weight of goods that need to be delivered at that node when selecting the next node.This improvement makes ants more intelligent and flexible,enabling them to better adapt to different cargo delivery needs.In addition,we adopt the reward and punishment mechanism as the pheromone update strategy of the ant colony algorithm,and use a pheromone volatilization factor that changes with the number of iterations to improve the convergence speed of the traditional ant colony algorithm.Finally,we validated the effectiveness of the model and algorithm through simulation experiments.Our research shows that by adding the weight of goods to be delivered at the next node to the vehicle cargo matching process,logistics routes can be better planned,transportation costs can be reduced,distribution efficiency can be improved,and the optimal matching scheme can be provided for logistics distribution centers.Finally,based on the system architecture,vehicle cargo matching model,and improved ant colony algorithm designed for the vehicle cargo matching system,we developed this distribution center vehicle cargo matching system based on clustering and improved ant colony search from the perspectives of cargo owners,distribution drivers,and distribution centers.After testing,the system has comprehensive functions and excellent matching performance,providing strong technical support for vehicle cargo matching in the distribution center.