基于机器学习的区域快递包装需求预测优化研究

Optimization of Regional Express Packaging Demand Prediction Based on Machine Learning

中文关键词: 快递包装  随机森林  需求预测

英文关键词: express packaging  RF  demand prediction

中文摘要:

      目的 快递包装需求预测精准程度是智慧物流体系化建设的重要基础，本文主要通过对不同机器学习方法、不同特征选择方法、不同参数寻优方法的比较，选取快递包装需求预测更加精准的方法，分析主要特征对快递包装需求的影响，优化区域快递包装需求的研究。方法 首先通过不同的机器学习方法进行比较;然后通过不同的特征选择方法进行比较，通过不同参数寻优方法比较确定遗传算法对优化随机森林模型参数的效果;最后为了更好地解释模型，引入SHAP分析的方法，对不同特征的重要性进行分析。结果 改进的随机森林预测模型效果最好，MAE值、MAPE值、RMSE值、R²分别为2 783、5.1%、4 343、0.99。女性人口和第三产业值是影响快递包装需求最为关键的因素。结论 所提出的预测方法有更好的准确性及可解释性，能为快递包装需求预测提供有力的决策支持。

英文摘要:

      The degree of accuracy of express packaging demand prediction is an important foundation for the construction of intelligent logistics systemization. The work aims to compare different machine learning methods, different feature selection methods, and different parameter optimization methods, to select a more accurate method of express packaging demand prediction, analyze the impact of the main features on demands of express packaging, and optimize the study of regional express packaging demand. Firstly, through comparison with different machine learning methods, different feature selection methods, and different parameter optimization methods, the effect of genetic algorithm on optimizing parameters of the random forest (RF) model was determined. Finally, in order to better explain the model, the method of SHAP analysis was introduced to analyze the importance of different features. The results showed that the improved random forest prediction model was the most effective with MAE value, MAPE value, RMSE value and R2 of 2 783, 5.1%, 4 343 and 0.99 respectively, and that female population and tertiary industry value were the most critical factors affecting the demand for express packaging. The results show that the proposed prediction method has better accuracy and interpretability, and can provide powerful decision support for express packaging demand prediction.