在大型商超线上订单分拣系统中，考虑分拣员之间工作量平衡，研究了订单分批与拣货路径优化问题。针对拣货员数量有限的“先拣后分”仓店一体分批拣货场景，构建考虑工作量平衡和拣货效率双目标的订单分批与拣货路径优化模型，结合K-means聚类和贪婪算法的最近邻点策略设计改进的NSGA-Ⅱ算法进行求解。基于沃尔玛等超市运作情况设置拣货布局和算例参数，通过不同规模的算例验证了模型正确性和算法有效性。实验结果表明，以极差作为平衡指标的双目标订单分批与拣货路径优化模型不仅可以实现工作量平衡而且对拣货效率的负面影响更小；当种群规模设置为50和迭代次数为100时算法可以获得优质解；基于“先拣后分”的订单分批与路径优化策略比按单分拣可以平均缩短订单最终完成时间44.37%。结论表明，双目标模型和算法有利于在工作量平衡和拣货效率两个目标之间取得平衡，既可以提高拣货效率，又可以满足拣货员之间工作量平衡的要求。

 In the online order picking system of large supermarkets, considering the workload balance among pickers, the optimization of order batching and picking route was studied. For the scenario of " sort-after-pick" batch picking in large supermarkets with a limited number of pickers during peak periods, a dual-objective optimization model was constructed with two objectives of minimizing total completion time and minimizing the range in completion times. According to the problem feature and the dual-objective solution method, an improved NSGA-II algorithm was designed by combining the K-means clustering algorithm and the nearest neighbor strategy of the greedy algorithm. Based on the practical operation of large supermarkets such as Walmart, a picking layout and case parameters were set. The correctness of the model and the effectiveness of the algorithm were verified through examples of different scales. Numerical experiments show that range as workload balance criteria in a dual-objective model not only achieve workload balance but also has fewer negative impacts on picking efficiency. A further sensitivity analysis indicates that a population size of 50 and an iteration number of 100 are conducive to obtaining quality solutions. Comparative experiments conducted on datasets of different sizes reveals that the "sort-after-pick" method can reduce the average completion time of orders by 44.37% in comparison to the single order picking strategy. The conclusion indicates that the dual-objective model and algorithm can achieve a balance between workload balance and picking efficiency, improving picking efficiency while satisfying workload balance requirement from pickers’ perspective.