摘要
供应链协同已经成为供应链集团在与其他集团之间日趋激烈的竞争中创造竞争优势的势在必行的现代管理战略,供应商选择对供应链协同至关重要。通过控制进化种群划分与进化过程,利用压缩变异与Gauss变异设计一种组合变异方式,进而提出改进的模糊C-均值聚类遗传算法(IFCMGA);在初步确定面向供应链协同的供应商评价指标后,利用IFCMGA算法对供应商协同能力评价指标进行分类,构建了面向供应链协同的供应商评价指标体系。结合模糊层次分析法与重要指标筛选法进行指标分析和筛选以及指标体系重构,以为供应商评价与选择提供科学决策依据。
Supply Chain Collaboration（SCC）has become a modern imperative management strategy to prepare a supply chain group for the increasingly fierce competition with other supply chain groups to create competitive advantage. As for SCC, supplier selection has significant importance. Through controlling the division of evolutionary population and the evolutionary process, a combinational mutation is designed with the use of Compression Mutation（CM）and Gauss Mutation （GM）and Improved Fuzzy C-means Clustering Genetic Algorithm（IFCMGA）is further proposed. Through analyses and expert investigations, the supplier evaluation indicators for SCC are preliminarily determined. IFCMGA algorithm is applied to classify these supplier collaboration capability evaluation indicators and the index system of supplier evaluation for SCC is built up with the classification results. Combining Fuzzy Analytic Hierarchy Process（FAHP）and important indictor screening method, indicators are analyzed and screened with reconstructed index system so as to provide a scientific decision making basis for the evaluation and selection of suppliers.
出处
《计算机工程与应用》
CSCD
2014年第4期18-23,共6页
Computer Engineering and Applications
基金
国家自然科学基金项目(No.71071165)
国家自然科学基金重点项目(No.51238008)
江西省科技支撑计划(No.20133BBG70104)
关键词
供应链协同
供应商评价指标体系
组合变异
改进的模糊C-均值聚类遗传算法
指标筛选
supply chain collaboration
index system of supplier evaluation
combinational mutation
Improved Fuzzy C-means Clustering Genetic Algorithm（IFCMGA）
indictor screening
