目的 针对目前烟草物流配送中心条烟分拣量大，不同条烟品规的分配对订单的总处理时间影响较大的问题，研究平衡各个分拣区品规的分配，提高分拣效率。方法 建立以各分区品规相似系数和最小为目标函数的数学模型，并采用改进的遗传粒子群动态聚类(GAPSO-K)算法进行求解。首先，结合各品规分拣量对品规相似系数进行改进，并将其作为适应度函数;然后在粒子群算法中对惯性权重因子进行改进，使其值可以进行自适应改变;最后，在粒子群动态聚类算法中引入遗传算法中的交叉变异扩大解的搜索范围，基于Matlab对文中的其他算法进行求解对比，求得结果在EM-plant中进行仿真验证。结果 结合某烟草物流配送中心数据仿真验证，利用GAPSO-K算法处理订单的时间为234.5 s，较传统时间大幅度较少，有效提升了柔性物流分拣效率。结论 采用该算法可充分发挥2种算法的优良性，具有更好的收敛性及寻优性，为柔性物流品规分配提供了新思路。

In order to solve the problems of the large sorting quantity of cigarette in tobacco logistics distribution center and the great impact of assignment of cigarette specification on the total processing time of orders, the work aims to study the allocation of each sorting zone and improve the sorting efficiency. A mathematical model with the objective function of minimizing the similarity coefficients of specification in each zone was developed and solved by an improved genetic particle swarm dynamic clustering (GAPSO-K) algorithm. Firstly, the similarity coefficient of each specification was improved by combining the sorting quantity of each specification as the fitness function. Then, the inertia weight factor was improved in the particle swarm algorithm so that its value could be changed adaptively. Finally, the cross-variance in the genetic algorithm was introduced in the particle swarm dynamic clustering algorithm to expand the search range of the solution, and the results were compared with the other algorithms based on Matlab. The results were simulated and verified in EM-plant. Combined with the data simulation verification in a tobacco logistics distribution center, the time for processing order with GAPSO-K algorithm was 234.5 s, which was significantly reduced compared with the traditional time, effectively improving the efficiency of flexible logistics sorting. The use of this algorithm can give full play to the goodness of both algorithms, with better convergence and merit-seeking, and provides a new idea for flexible logistics product rule allocation.