基于机器学习的电商物流末端配送需求预测与调度优化研究

Research on Demand Forecasting and Scheduling Optimization for E-Commerce Logistics Last-Mile Delivery Based on Machine Learning

摘要: 随着电子商务的快速发展，末端配送已成为配送成本的主要构成部分和运营效率的关键瓶颈。传统调度方法大多依赖静态规则或人工经验，难以有效应对电商订单需求波动带来的不确定性。本文旨在构建一种融合机器学习预测与优化调度的决策框架，以降低电商物流末端配送成本、提升时效达成率。本文基于某电商平台城市配送中心的历史订单数据，提取时间、天气、区域等多维特征，构建XGBoost、LightGBM、随机森林和LSTM四种机器学习模型，对比预测精度并选取最优模型。将预测结果嵌入调度决策流程，采用将预测分布信息纳入调度决策的方法处理预测不确定性，形成预测驱动的调度策略，并通过电商平台真实数据仿真验证。XGBoost在预测精度上优于对比模型，平均绝对误差为23.6件/小时。本文提出的预测–调度一体化方法较人工调度降低配送成本20.2%，较静态调度降低10.8%；时效达成率提升至91.8%，较人工调度提高13.3个百分点。在电商大促期间，该方法有效缓解了订单峰值带来的配送压力。

Abstract: With the rapid development of e-commerce, last-mile delivery has become the main component of delivery costs and a key bottleneck for operational efficiency. Traditional scheduling methods mostly rely on static rules or manual experience, which are unable to effectively cope with the uncertainty brought about by fluctuations in e-commerce order demands. This paper aims to construct a decision framework that integrates machine learning prediction and optimization scheduling to reduce the cost of e-commerce logistics last-mile delivery and improve the on-time delivery rate. Based on the historical order data of a certain e-commerce platform’s urban distribution center, this paper extracts multi-dimensional features such as time, weather, and region, and builds four machine learning models: XGBoost, LightGBM, random forest, and LSTM. It compares the prediction accuracy and selects the optimal model. The prediction results are embedded into the scheduling decision process, and the method of incorporating the prediction distribution information into the scheduling decision is adopted to handle the prediction uncertainty, forming a prediction-driven scheduling strategy, and verified through real data simulation of the e-commerce platform. XGBoost outperforms the comparison models in terms of prediction accuracy, with an average absolute error of 23.6 items per hour. The proposed prediction-scheduling integrated method reduces the delivery cost by 20.2% compared to manual scheduling and by 10.8% compared to static scheduling; the on-time delivery rate is increased to 91.8%, which is 13.3 percentage points higher than manual scheduling. During e-commerce promotions, this method effectively alleviates the delivery pressure caused by peak orders.