In order to discuss the vehicle routing problem, which is a combination optimization problem, we apply drosophila algorithm. First of all, we introduces the fundamental concepts of drosophila algorithm and describes the specific implementation steps. Secondly, we analyze vehicle routing problem and mathematical model and do a simulation experiment to explore the effect and performance of drosophila algorithm. The experimental results show that the improved drosophila algorithm, compared with the common drosophila algorithm, can improve the solution accuracy indeed, and it efficiently solves the vehicle routing problem. As a result, it can be concluded that, in order to effectively solve the typical combination optimization problem-vehicle routing problem, it is necessary to apply artificial intelligent method, which has rather good performance and it can improve the accuracy of algorithm.

Abstract. In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.