Study On Intelligent Algorithm For Vehicle Scheduling In Logistics Distribution

As our country's paces towards industrialization speed up, logistics, which is one of the pillars of industrial and commercial enterprises, is gaining more and more attentions. Especially in recent years, the thriving of the third-part logistics companies call for logistics theories and technologies of high standards. As one of the basic parts of logistics, transportation is playing an important role in logistics operation. Transportation accounts for a large portion of logistics cost. So, the vehicle scheduling theory, which is an important part of logistics management, has become a hot topic of the scholar field.This paper is composed of two sections. The first part deals with the scheduling algorithms of the static vehicle scheduling problems. This section mainly researches three algorithms which are Immune algorithm, Particle swarm algorithm and Ants algorithm which are about vehicle routing problems in a static state and work out the performance qualities of the different algorithms applied to different problems through analyzing the mechanics of the algorithms and solving the Solomon's instances. And then summarize which kind of algorithm should be used to solve certain problem in order to get the most satisfied solution. At last, the root mechanics of the algorithms are interpreted from the angle of genetic algorithm.The second section proposed a new dynamic vehicle routing problem. The hypothesis of the original one is that the time vehicles take to pass a road is subjected to normal distribution with constant mean values and variances, however it apparently does not meet the real situation. The new model forms a new hypothesis, that the normal distribution varies according to the time when vehicles get to the road, and stochastic demand is combined. Aimed at this problem, this paper built a stochastic chance constrained programming model and designed a genetic algorithm to solve this problem, and an instance is given.