Multi-objective optimization and evaluation method of modular product configuration design scheme
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- 作者：Wei Wei (1)
Wenhui Fan (2)
Zhongkai Li (3) - 关键词：Configuration optimization ; Modularized design ; Multi ; objective optimization ; Pareto set ; Air compressor
- 刊名：The International Journal of Advanced Manufacturing Technology
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：75
- 期：9-12
- 页码：1527-1536
- 全文大小：1,062 KB
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Wenhui Fan (2)
Zhongkai Li (3)
1. Advanced Manufacturing Technology and Systems Research Center, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China
2. Department of Automation, Tsinghua University, Beijing, 100084, China
3. School of Mechatronics Engineering, China University of Mining and Technology, Xuzhou, 221116, China - ISSN：1433-3015
文摘
Product configuration is one of the key technologies for mass customization. Traditional product configuration optimization targets are mostly single. In this paper, an approach based on multi-objective genetic optimization algorithm and fuzzy-based select mechanism is proposed to solve the multi-objective configuration optimization problem. Firstly, the multi-objective optimization mathematical model of product configuration is constructed, the objective functions are performance, cost, and time. Then, a method based on improved non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the configuration design optimization problem. As a result, the Pareto-optimal set is acquired by NSGA-II. Due to the imprecise nature of human decision, a fuzzy-based configuration scheme evaluation and select mechanism is proposed consequently, which helps extract the best compromise solution from the Pareto-optimal set. The proposed multi-objective genetic algorithm is compared with two other established multi-objective optimization algorithms, and the results reveal that the proposed genetic algorithm outperforms the others in terms of product configuration optimization problem. At last, an example of air compressor multi-objective configuration optimization is used to demonstrate the feasibility and validity of the proposed method.