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基于遗传算法的车辆路径优化问题，虽然已经被提出过多种思想方法，但仍有较大的改进余地．而且具体的车辆路径优化问题，往往在时间和路程两方面都有限制和要求，而在这方面的研究相对较少．针对这种问题，在传统遗传计算基础上，加入了时间约束算法，对算法的实时实现作出保证；同时根据客户点的位置改进初始群，提高了变异率，减少低效计算，极大提高了寻优速度，为多计算点的遗传操作提供有力的支持．计算机仿真实现的结果和物流公司的实际运作都证明了算法的有效性．

Optimizing Vehicle Routing Problem with Time Constrains Based on Genetic Algorithm

Mthough many thoughts about vehicle routing problem bases on genetic algorithm, there is still large space for its improvement. Faced to concrete routing problem, time and routs are both restricted and required and researches in this field are not enough. This paper, based on traditional genetic algorithm, added time restriction algorithm to guarantee the algorithm is achieved in real time. It improved the initial population in view of client distribution position, raised variation rate, reduced low efficiency calculation and improved optimistic speed greatly. It also provided strong support to multi-clients genetic computing. The efficiency of this algorithm was proved by the results of computer simulation and practical operation by logistic companies.