Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

作     者：Ling Tan 

作者机构：Department of E-CommerceZhejiang Business CollegeHangzhou 310053China 

出 版 物：《Intelligent and Converged Networks》 (智能与融合网络（英文）)

年 卷 期：2023年第4卷第2期

页      面：168-180页

核心收录：

学科分类：1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Levenberg–Marquardt Backpropagation(LMBP)algorithm Cross Border E-Commerce(CBEC) supply chain performance evaluation evaluation indicators artificial fish swarm algorithm 

摘      要：In recent years,with the support of national policies,Cross Border E-Commerce(CBEC)has developed rapidly.This business model not only brings significant benefits to the national economy,but also has many unique challenges,especially at the level of supply chain management.Therefore,to enable CBEC enterprises to develop sustainable supply chain,this study discusses the performance evaluation model of supply chain and proposes a CBEC Supply Chain Performance Evaluation Model(CBECSC-EM)based on the Levenberg–Marquardt Backpropagation(LMBP)algorithm.This experiment constructs performance evaluation indicators for the supply chain of CBEC enterprises.On this basis,the LMBP algorithm is introduced,and improved in the experiment to make the overall performance of the evaluation model more scientific and reasonable.In the verification set,the maximum F1 values of LMBP,DEA,SBM,and BP are 98.46%,93.78%,87.29%,and 78.95%,respectively.The MAPE value of LMBP model is 0.102%,which is lower than the other three methods(0.282%,0.343%,and 0.385%)selected in the experiment.The maximum standard deviation rates of importance and operability of the evaluation indexes are 0.1346 and 0.1405,respectively,and there is a significant consistency between the expert scores.Therefore,the LMBP algorithm has broad application prospects in supply chain performance evaluation of CBEC enterprises.