Vehicle Routing Optimization Method Based On Genetic Aigorithm Research And Application

Distribution as a new logistics means, both for the development of enterprises, or the whole economic and social benefits, has an important role. In the distribution cost in logistics, transportation and distribution costs account for a large proportion of vehicle. But in the logistics facing customer networks continues to increase, the traffic is blocked, a series of problems such as vehicles increased, causing the vehicle to low efficiency of management, distribution costs, an increasing number of emergency, corporate profits increasingly decrease. So this paper focuses on vehicles in logistics distribution path optimization is studied. The genetic algorithm is introduced in VRP based on traditional vehicle routing optimization, the genetic operators of crossover and mutation, duplication, genetic algorithm is introduced into the ant colony algorithm, using ant colony algorithm to solve the problem of logistics distribution path optimization, and improved pheromone update methods, customer selection strategy, in order to improve the convergence speed and the global searching ability of the algorithm, can a good solution to these problems. The specific research contents as follows..The paper studies physical distribution routing problem of Huamulan CSA in details. Through the analysis of the present situation of mulan farm community distribution, points out its distribution in the path optimization problems. Then use the AHP method to determine the path optimization target, in order to build corresponding model to solve the problem required. Next, systematically analysis and research the traditional vehicle routing optimization method, comparing the advantages and disadvantages. Meanwhile, analysis genetic algorithm for the present situation of the application, in order to make theoretical preparation for the new vehicle routing optimization method. Finally, the above traditional vehicle routing optimization method can’t deal with business needs and distribution network that will increase vehicle scheduling bottleneck problem. So on the basis of introducing, the genetic algorithm is proposed in the replication, mutation operator to the ant colony algorithm, such as by improving customer selection strategy, pheromone changes the way to improve the convergence speed and search for a more optimal solution path optimization model based on ant colony algorithm. Combining with an instance of Huamulan CSA, through the analysis found that the new algorithm can quickly update the distribution site, within the shortest time of vehicle scheduling planning, greatly reducing the cost of vehicle distribution. So it has a certain theoretical and application value.