摘要
针对现有的企业绩效评价研究存在评价指标体系维度单一、评价结果时效较短、缺乏对企业未来绩效水平的探讨等问题,以我国上市物流企业为例,从财务及非财务角度构建了我国上市物流企业绩效评价指标体系,并通过熵权–VIKOR算法确定了各评价指标的权重以及各样本企业的期望绩效值。同时,采用自适应遗传算法(adaptive genetic algorithm,AGA)对传统的BP神经网络进行优化,构建了基于AGA-BP神经网络的企业绩效评价和预测模型。最后以36家样本企业数据为基础,对该模型进行训练和测试。测试结果证明了基于AGA-BP神经网络的上市物流企业绩效预测模型的有效性和实用性。
In view of the problems in the existing research on enterprise performance evaluation,such as the single dimension of the evaluation index system,the short timeliness of the evaluation results,and the lack of discussion on the future performance level of enterprises,China’s listed logistics enterprises was taken as an example to construct the performance evaluation index system of China’s listed logistics enterprises from the financial and non-financial perspectives.The weight of each evaluation index and the expected performance value of each sample enterprise were determined by entropy-VIKOR algorithm.At the same time,an enterprise performance evaluation and prediction model based on AGABP neural network was constructed through the optimization of BP neural network using adaptive genetic algorithm(AGA).Finally,based on the data of 36 sample enterprises,the model was trained and tested.The test results show that the performance prediction model of listed logistics enterprises based on AGA-BP neural network is effective and practical.
作者
曾凡龙
倪静
王钰华
ZENG Fanlong;NI Jing;WANG Yuhua(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China;Ningbo Jintian Copper Group,Ningbo 315034,China)
出处
《上海理工大学学报》
CAS
CSCD
北大核心
2022年第1期94-102,共9页
Journal of University of Shanghai For Science and Technology
基金
国家自然科学基金资助项目(71774111)
教育部人文社会科学基金资助项目(19YJAZH064)。
