为了研究物流中心的服务效率和车辆的合理调度方案，以汽车载重量作为影响车辆路线安 排的主要因素，以经典的车载容量约束条件下的车辆路径问题为原型建立数学模型，通过求解该 数学模型的最优解来获得车辆最优路径。由初始状态随机生成的可行解作为初始的车辆路径方 案，通过改进的遗传算法不断地调整染色体的交叉和变异概率进行优化，最终得到物流中心车辆 安排的合理方案。通过多次求解算例，都能够得到满意的车辆路径方案，不仅验证了该数学模型 的有效性和实践性，而且也验证了改进后遗传算法的收敛性和鲁棒性，同时得到了改进遗传算法 交叉和变异概率的调整范围。该模型和算法不仅可以提高物流中心的服务效率，而且可以为物流 中心的车辆调度方案提供支持和帮助。

In order to research the service efficiency and reasonable scheduling scheme of vehicles of a logistics center, the vehicle load was taken as the main factor which influenced the vehicle routing ar⁃ rangement, the classical capacitated vehicle routing problem was used as the prototype to establish the mathematical model, and the optimal path was obtained by solving the optimal solution of the mathemati⁃ cal model. The initial feasible solution generated randomly served as the initial vehicle routing plan, a reasonable vehicle arrangement plan of logistics center was found finally by constantly adjusting chromo⁃ somal crossover and mutation probability of the improved genetic algorithm to optimize the solution. It verifies the effectiveness and practicality of the proposed model, the convergence and robustness of the improved genetic algorithm by solving a numerical example for many times. Meanwhile the adjusting range of crossover and mutation probability of the improved genetic algorithm are obtained. The model and algorithm not only improves the service efficiency of logistics center, but also provide support and help for the vehicle scheduling scheme of logistics center.