中文摘要：

分析并构建了库存不足条件下车辆路径问题的数学模型；在模型的求解上，提出一种基于子群协作的动态粒子群算法；最后通过算例实验表明：该算法能有效克服标准粒子群算法迭代寻优时选择步长的盲目性，也改善了算法求解时容易陷入局部最优、导致早熟的缺陷，具有较强的全局寻优能力，收敛速度快，计算精度高。

英文摘要：

This paper analyzed and established mathematic models for vehicle routine problem under the condition of stock shortage. To solve the models,it presented a dynamic particle swarm optimization algorithm based on sub-group collaboration. Finally, the paper made some experimental calculations, and the results of calculations proved that the algorithm eould avoid blind search effectively, and overcome the limitation of easily trapping in local extreme points and leading to premature, as a result, it had better capability of global optimization, higher speed of convergence and precision than standard particle swarm optimization.