目的 以轿运车使用数量最少为目标研究整车物流中乘用车的装载问题，以降低物流成本。方法 建立轿运车混合装载的数学模型，并用枚举法列出混合装载的所有装载方案；筛选装载率在90%以上的方案建立组合矩阵，以此和乘用车的数量类型等作为约束条件，建立求解轿运车最少数量的数学模型；用遗传算法求最优解，并对计算结果进行验证。结果 仿真结果表明遗传算法计算得到的最优配载方案与枚举出的最优解相近，但遗传算法计算时间仅为枚举法计算时间的1/200左右。结论 用遗传算法对整车物流中乘用车的装载问题求最优方案的方法收敛速度快，计算结果与理论最优解相近，可兼顾计算时间和计算效果。

The work aims to study the loading problem of vehicle logistics with the minimum number of car carrier as the goal so as to reduce logistics cost of the car. Mathematical model of the mixed loading of the car was established firstly. Different mixed loading schemes were compared and analyzed by enumeration algorithm. Schemes with loading rate more than 90% were screened out to establish a combination matrix. Then, the mathematical model of the minimum number of car carrier was set up by using the car's full load scheme and the cars'number and cars'type as the constraint conditions. Finally, the genetic algorithm was used to solve the optimal soluton, and the calculation results were verified. The simulation results showed that the optimal loading scheme calculated by genetic algorithm was close to the optimal solution obtained by enumeration, but the computing time of the former was only about 1/200 of the latter. In conclusion, the genetic algorithm has fast convergence and closer results with the theoretical optimal solution. It has advantages of both computing time and calculation effect.