Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICARA.2000.4803951
Title: Modified GA-based optimizer for multi-objective product family design
Authors: Zhuo, L. 
San, W.Y. 
Seng, L.K. 
Keywords: Genetic algorithm
Multi-objective optimization
Product family design
Issue Date: 2009
Source: Zhuo, L.,San, W.Y.,Seng, L.K. (2009). Modified GA-based optimizer for multi-objective product family design. ICARA 2009 - Proceedings of the 4th International Conference on Autonomous Robots and Agents : 121-126. ScholarBank@NUS Repository. https://doi.org/10.1109/ICARA.2000.4803951
Abstract: Product family design has been recognized as an effective method to satisfy diverse customer's demands costeffectively. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss compared to individual design. In this paper, a modified genetic algorithm using dynamic weighted aggregation is proposed to optimize a scale-based product family design while making the two-objective (performance-and-commonality) optimization tractable and efficient. The proposed method not only overcomes the drawbacks of conventionally fixed weight aggregation for product family design, but also maintains the computation expense at the economical level. An example of designing a family of planetary gear trains is presented to demonstrate the proposed method. ©2009 IEEE.
Source Title: ICARA 2009 - Proceedings of the 4th International Conference on Autonomous Robots and Agents
URI: http://scholarbank.nus.edu.sg/handle/10635/50787
ISBN: 9781424427130
DOI: 10.1109/ICARA.2000.4803951
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

21
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.