Research on Vehicle Scheduling Problem Based on Cloud Model

Abstract

Considering the multi-distribution centers vehicle scheduling problem effectiveness and real-time requirements, the cloud genetic algorithm was introduced by the combination of cloud model theory and genetic algorithms. Make use of normal cloud mode has the characteristics of universal and cloud droplets has the characteristics of random and stability tendentious, cloud model X-condition cloud generator algorithm to generate adaptive crossover and mutation probability in the process of evolutionary search.. Cloud genetic algorithms improve the algorithm convergence, robustness and the solutions quality. And also it overcomes the traditional genetic algorithm shortcomings such as slow searching, easy to local optimization solutions. Finally, this paper analyzes and validates the vehicle scheduling problem by using CGA. Then compares CGA with traditional method and the overall method, and by experimental analysis we can find that CGA is superior to the other two methods on the aspect of efficiency and the results.