A Hybrid DEA-Adaboost Model in Supplier Selection for Fuzzy Variable and Multiple Objectives

被引：14

作者：

Cheng, Yijun ^{[1
]}

Peng, Jun ^{[1
]}

Zhou, Zhuofu ^{[1
]}

Gu, Xin ^{[1
]}

Liu, Weirong ^{[1
]}

机构：

[1] Cent South Univ, Changsha 410075, Hunan, Peoples R China

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Data-Driven Decision Making; Supply Logistics; Supplier selection; DEA; Machine learning; Adaboost;

D O I：

10.1016/j.ifacol.2017.08.2038

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：12255 / 12260

页数：6

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