目的 为了解决电商企业订单到达的不确定性和仓储运营的特殊性造成的人员配置不合理问题，提出一种构建基于订单预测和产线平衡为目标的Monte Carlo季节指数平滑和作业产能模型。方法 应用概率统计方法解决信息不完全下订单预测问题。通过该方法对季节指数平滑法中的平滑系数进行优化，以修正预测模型，然后用Crystal ball软件对预测值进行产线调度优化。结果 算例分析表明，使用该方法进行预测时，精度提高了45%，并将预测值用于拣选作业产能安排，确定了最优人员数量和工时分配方案。结论 可以为电商类企业提供准确的订单预测信息，以及合理的作业人员配置方案，提高了企业的运行效率。

The paper aims to propose a Monte Carlo seasonal exponential smoothing and job capacity model based on order forecasting and production line balance, to solve the problem of unreasonable staffing caused by the uncertainty of the order arrival of e-commerce enterprises and the particularity of warehousing operations. The probabilistic statistical method was used to solve the problem of incomplete ordering. The smoothing coefficient in the seasonal exponential smoothing method was optimized by this method to modify the prediction model. And then the software-Crystal ball was used to optimize the production line scheduling. The analysis of the example showed that when this method was used for prediction, the accuracy was improved by 45%. The predicted value was used for the sorting operation capacity arrangement and the optimal number of people and the distribution of working hours were determined. It can provide accurate forecasting information for e-commerce companies, as well as reasonable staffing solutions to improve business efficiency.