基于RF-GA-BPNN算法的供应链风险预警研究  

Early Warning of Supply Chain Risks Based on RF-GA-BPNN Algorithm

作　　者：王红春[1] 周子祥 WANG Hongchun;ZHOU Zixiang(School of Urban Economics and Management,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)

机构地区：[1]北京建筑大学城市经济与管理学院,北京100044

出　　处：《工业工程》2025年第2期120-128,共9页Industrial Engineering Journal

基　　金：国家自然科学基金资助项目(61772062);国家“十四五”重点研发计划资助项目(2022YFB3305600)。

摘　　要：供应链系统时刻面临着来自内外部环境的多重风险与挑战,目前供应链风险预警算法在指标选取、阈值优化等方面尚存不足。为进一步提升供应链风险预警能力,关注算法融合优化及其预警效果,构建基于RF-GABPNN算法的供应链风险预警模型。该模型有机结合随机森林、遗传算法、BP神经网络等多类算法的特性与优势,通过指标特征重要性筛选、初始参数优化等手段改进BP神经网络预测效果。利用中国A股3309家上市企业的风险预警指标数据集对模型进行训练与测试,结果表明RF-GA-BPNN算法在300组随机样本数据的训练下,预警准确率可达96.50%。基于RF-GA-BPNN算法的供应链风险预警模型具有较优秀的学习能力和预警能力,预测结果可为供应链风险水平的初期判断以及风险抵御措施的制定实施提供数值参考。Supply chain systems are constantly facing multiple risks and challenges from both internal and external environments.In the existing research,early warning algorithms of supply chain risks are still insufficient in terms of indicator selection and threshold optimization.In order to further improve the early warning ability of supply chain risks,this paper establishes an early warning model based on the RF-GA-BPNN algorithm focusing on algorithm fusion optimization and its warning effectiveness.The model organically combines the characteristics and advantages of multiple algorithms,including random forest,genetic algorithm,BP neural networks,to improve the prediction effect of the BP neural network by means of indicator feature importance screening and initial parameter optimization.The model is trained and tested by using a risk early warning indicator dataset from 3,309 listed companies in Chinese A-share market.The prediction results show that the RF-GA-BPNN algorithm achieves an early warning accuracy of 96.50%after training with 300 sets of random sample data.The proposed model based on the RF-GA-BPNN algorithm has excellent learning and early warning capabilities.The prediction results provide numerical references for the initial judgment of supply chain risk levels and the implementation of risk resistance measures.

关 键 词：供应链 风险预警 随机森林 遗传算法 BP神经网络 

分 类 号：F272.1[经济管理—企业管理] TP391[经济管理—国民经济]