论文部分内容阅读
基于低碳供应链环境新常态特点,通过实地调研等方式收集一二手资料,利用粗糙集及主成分因子法改进扎根理论法挖掘资料,采用探索性及验证性因子分析法评判范畴拟合度和可信度,保证指标选取的理论性及实际性。运用基于模糊理论的混合多维层次分析法、熵值法及数据包络法综合确定指标权重,确保权重的主客观合理性。再设计基于主成分分析法的径向基神经网络法,提取训练数据中的隐性知识,总结规律,以有效提升供应商评价及选择动态性与拓展性。实例比较研究表明,该评价选择模型具有很好的理论及应用价值。
Based on the new normality in the environment of low-carbon supply chain, primary and secondary sources were collected through on-the-spot investigation and other methods. Rough sets and principal components factor method were used to improve the grounded theory to mine the data. Exploratory and confirmatory factor analysis And credibility, to ensure that the index selected theoretical and practical. This paper uses mixed multi-dimensional analytic hierarchy process (AHP) based on fuzzy theory, entropy method and data envelopment method to determine the weight of indicators and ensure the subjective and objective rationality of weights. The radial basis function neural network method based on principal component analysis is designed to extract the tacit knowledge in training data and to summarize the law so as to effectively enhance the supplier evaluation and selection of the dynamic and expansibility. The comparative study of examples shows that the evaluation selection model has good theoretical and practical value.