作者:Luan, J (Luan, Jing)[ 1 ] ; Yao, Z (Yao, Zhong)[ 2 ] ; Zhao, FT (Zhao, Futao)[ 2 ] ; Song, X (Song, Xin)[ 3 ]
MATHEMATICS AND COMPUTERS IN SIMULATION
卷: 156页: 294-309
DOI: 10.1016/j.matcom.2018.08.011
出版年: FEB 2019
文献类型:Article
摘要
Nowadays, with the development of information technology and economic globalization, supplier selection problem gets more and more attraction. The recent literature shows huge interest in hybrid artificial intelligence (AI)-based models for solving supplier selection problem. In this paper, to solve a multi-criteria supplier selection problem, based on genetic algorithm (GA) and ant colony optimization (ACO), hybrid algorithm of GA and ACO is developed. It combines merits of GA with great global converging rate and ACO with parallelism and effective feedback. A numerical experiment was conducted to optimize parameters and to analyze and compare the performance of the original and hybrid algorithms. Results demonstrate the quality and efficiency improvement of new integrated algorithm, verifying its feasibility and effectiveness. It is an innovative pilot research to leverage hybrid AI-based algorithm of GA and ACO to settle the supplier selection problem, which not only makes a clear methodological contribution for optimization algorithm research, but also can be served as a decision tool and provide management reference for companies.(C) 2018 International Association for Mathematics and Computers in Simulation(IMACS). Published by Elsevier B.V. All rights reserved.
关键词
作者关键词:Supplier selection; Hybrid algorithm; Genetic algorithm; Ant colony optimization
KeyWords Plus:PARTICLE SWARM OPTIMIZATION; ANALYTIC NETWORK PROCESS; PARTNER SELECTION; FUZZY TOPSIS; DECISION; MODEL; DESIGN; UNCERTAINTY; EFFICIENCY; LOGIC
通讯作者地址:
Beihang University Beihang Univ, Sch Econ & Management, 37 Xueyuan Rd, Beijing 100191, Peoples R China.
通讯作者地址: Yao, Z (通讯作者)
