An Improved Ant Colony Algorithm For Vehicle And Cargo Matching Of Freight Logistics Platform

With the spectacular growth of China's economy,logistics requirements has increased rapidly and transport capacity has been challenged.Moreover,China's logistics industry still faces such issues as information asymmetry between vehicle drivers and cargo owners,idle vehicles and empty return trips,which result in high freight transportation costs.However,the rise of Internet,especially mobile Internet,has brought new opportunities to increase transport efficiency.One of the new opportunities is vehicle-cargo matching,which can effectively improve transportation efficiency and reduce transportation costs through information sharing and reasonable vehicle-cargo assignments on the freight logistics platform.Since it is difficult for a logistic service platform to create matches automatically between vehicles and cargoes and achieve high vehicle utilization,we investigate an intelligent approach to generate vehicle-cargo matches and recommend them to the corresponding cargo owners and vehicle drivers.Firstly,we develop a mathematical model for the vehicle-cargo matching problem,and we choose loading rate,empty loaded rate,time and other factors as the matching metrics.Those indexes are objective and easy to obtain,which can effectively avoid the circumstance where some vehicles can hardly be recommended owing to the lack of evaluation indexes such as reputation.Secondly,we propose an improved ant colony algorithm based on parameter adaptive adjustment.An improved k-means algorithm is used to judge the state of the ant colony,and the parameters are tuned adaptively to make the algorithm converge rapidly to the neighborhood of the global optimal solution.The parameters are then tuned based on the randomness and ergodicity of chaos,beneficial to jump out of local optimum.Finally,through extensive computational study,we demonstrate that the performance of the improved ant colony algorithm is better than that of the basic ant colony algorithm.The results suggest that the proposed algorithm can quickly provide vehicles and cargoes matches from lots of vehicles and cargoes sources,thus improving the overall transport efficiency,and allocating freight transport resources rationally.