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CLC number: TH12

On-line Access: 2015-01-04

Received: 2014-09-02

Revision Accepted: 2014-12-08

Crosschecked: 2014-12-25

Cited: 4

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Citations:  Bibtex RefMan EndNote GB/T7714






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Journal of Zhejiang University SCIENCE A 2015 Vol.16 No.1 P.1-10


A multi-principle module identification method for product platform design

Author(s):  Wei Wei, Ang Liu, Stephen C. Y. Lu, Thorsten Wuest

Affiliation(s):  School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; more

Corresponding email(s):   weiwei@buaa.edu.cn, angliu@usc.edu

Key Words:  Module identification, Modularization principles, Multi-objective optimization, Improved strength Pareto evolutionary algorithm (ISPEA2), Turbo expander

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Wei Wei, Ang Liu, Stephen C. Y. Lu, Thorsten Wuest. A multi-principle module identification method for product platform design[J]. Journal of Zhejiang University Science A, 2015, 16(1): 1-10.

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%T A multi-principle module identification method for product platform design
%A Wei Wei
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T1 - A multi-principle module identification method for product platform design
A1 - Wei Wei
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A1 - Stephen C. Y. Lu
A1 - Thorsten Wuest
J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1400263

In today’s competitive global business environment, platform strategy presents an opportunity for manufacturing companies to juggle increased customer demand for customized products and the inherited complexity and increased development cost that comes with it. The goal of this paper is to support module identification as an essential part of a module-based platform strategy approach. Based on various existing methods, this paper abstracted three principles, which include an internal clustering principle, an external independence principle, and an overall stability principle. The three principles should be holistically considered, and be simultaneously satisfied during the module identification. Both conceptual and mathematical modeling of the proposed multi-principle module identification method are elaborated. Then an improved strength Pareto evolutionary algorithm (ISPEA2) is used to address the multi-principle module identification problem and find the Pareto-optimal set. A fuzzy compromise selection method base on fuzzy set theory is also used to select the best compromise Pareto solution. An industrial case study in a turbo expander manufacturing company is provided to illustrate practical applications of the research. Finally, the result obtained by the proposed approach is compared with other established optimization approaches.



关键词:块划分;模块化准则;多目标优化; 改进的强度帕累托进化算法;透平膨胀机

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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