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research-article

An improved group leader algorithm for cloud-based material selection in product design with energy consideration

[+] Author and Article Information
Luning Bi

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, P. R. China
biluning@buaa.edu.cn

Ying Zuo

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, P. R. China
zuoying226@126.com

Fei Tao

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, P. R. China
ftao@buaa.edu.cn

T. Warren Liao

Department of Mechanical and Industrial Engineering, Louisiana State University, LA 70803, USA
ieliao@lsu.edu

Zhuqing Liu

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, P. R. China
liuzhuqing012@126.com

1Corresponding author.

ASME doi:10.1115/1.4035675 History: Received February 19, 2016; Revised December 23, 2016

Abstract

Energy consumption in manufacturing has risen to be a global concern. Material selection in the product design phase is of great significance to energy conservation and emission reduction. However, because of the limitation of the current life cycle energy analysis and optimization method, such concerns have not been adequately addressed in material selection. To fill in this gap, a process to build a comprehensive multi-objective optimization model for automated multi-material selection (MOO-MSS) on the basis of cloud manufacturing is developed in this paper. The optimizing method, named LS-DGLA, is a hybrid of differential evolution and local search with the group leader algorithm (GLA), constructed for better flexibility to handle different needs for various product designs. Compared with a number of evolutionary algorithms and non-evolutionary algorithms, it is observed that LS-DGLA performs better in terms of speed, stability and searching capability.

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