报告题目：A Hybrid DEA-Adaboost Model inSupplier Selection for Fuzzy Variable and Multiple Objectives
报告摘要：Abstract—Supplier selection is a critical multi-criteria decision making problem for supply chain management. With the emergence of big data, there is an urgent need for data-driven decision making methods. A hybrid DEA-Adaboost model is proposed to meet the challenge. The proposed model is split into the DEA and the learner. The fuzzy multi-objective DEA is used to build the expert database, which contains the appropriate and inappropriate suppliers. The learner is trained by Adaboost from the expert database. Thus, the DEA and derived learner are combined as the hybrid model to reduce the time consumption and computational complexities for suppliers selection. The simulation results demonstrate that the proposed model improves the accuracy compared with other two approaches.