Abstract: In order to solve the logistics supply and demand information asymmetry and improve the efficiency of logistics business docking, a lot of public logistics information platforms or systems have been built to post and share the logistics information. There exists relative research about how platforms passively response user's query for logistics supply and demand information, but the research about how to match vehicle and cargo information proactively and intelligently is very few.Vehicle and cargo matching problem is regarded as a kind of combinatorial optimization problems in this paper, a mathematical model has been established, and the model declares two decision variables:the constraints and the objective function. A kind of quantum evolutionary algorithm has been designed and proposed to solve the vehicle and cargo matching problem, which is improved by the method of the attenuation fitness with constraint punishment. An index, Quantum Swarm Maturity Value (QSMV), is introduced as a reference criteria for the quantum evolutionary algorithm exit. Vehicle and cargo matching problem based on quantum evolutionary algorithm can be solved into six steps, including:quantum group initialization, fitness calculation, selection of the optimal quantum individual, the judgment of algorithm exit, quantum group evolution and the optimal individual decoding. In the experiment, experimental data is given a set composed of 5 vehicles and 7 cargos. The exact solution is obtained by using the traversal method, which takes 6 hours and the fitness is 0.283226. An optimal solution is obtained by the quantum evolutionary algorithm, which takes 0.656 seconds and the fitness is 0.2832. Furthermore, the improved quantum evolutionary algorithm is compared with the standard genetic algorithm, experiment results show that quantum evolutionary algorithm's convergence speed is increased by 58%, average error is reduced by 86% and stability is increased by 32%. Experiment results also show that quantum group scale has "bottleneck" problem, larger quantum group scale does not improve the algorithm performance obviously, and quantum rotation angle increment is positive correlation to algorithm convergence speed, and negatively correlated to global search ability.The results show that the improved quantum evolutionary algorithm can efficiently get the optimal solution for the vehicle and cargo matching problem, and enable the public logistic information platform to intelligently recommend reasonable supply or demand information for different users, and help users reduce the idle vehicles rate and empty-run rate, and improve the utilization ratio of logistics information resources.

Key words: vehicle and cargo matching, quantum evolutionary algorithm, logistic information platform