Research on the Staff Scheduling Strategy of the Sorting Center in the Logistics Network

在电商物流网络中，分拣中心货量预测及人员排班对整体网络运作意义重大。以K公司华南区SC1分拣中心为例，确定人效、班次、人数限制等参数，并进行数据采集和预处理。接着建立CNN-LSTM-AM预测模型对分拣中心每日和每小时货量进行预测，同时基于预测货量建立人员排班模型，求解得到最优排班方案。结果显示货量预测误差均值在7%，且最优排班结果各班次实际小时人效均衡、正式工安排合理，有助于提升分拣中心管理效率、降低成本。In the e-commerce logistics network, the prediction of the volume of goods in sorting centers and the staff scheduling are of great significance to the overall network operation. Taking the SC1 sorting center in South China of Company K as an example, this paper determines parameters such as labor efficiency, shifts, and personnel limits, and conducts data collection and preprocessing. Then, a CNN-LSTM-AM prediction model is established to predict the daily and hourly volume of goods in the sorting center. Meanwhile, based on the prediction of the volume of goods, a staff scheduling model is established, and the optimal scheduling solution is obtained through solving. The results show that the average error of the prediction of the volume of goods is 7%, and the actual hourly labor efficiency of each shift in the optimal scheduling result is balanced, and the arrangement of full-time workers is reasonable, which helps to improve the management efficiency of the sorting center and reduce costs.