Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10845-009-0365-8
Title: A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm
Authors: Liu, Z. 
Wong, Y.S. 
Lee, K.S. 
Keywords: Commonality index
Design for manufacturing
Genetic algorithm
Multi-platforming
Product family
Issue Date: Dec-2011
Source: Liu, Z., Wong, Y.S., Lee, K.S. (2011-12). A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm. Journal of Intelligent Manufacturing 22 (6) : 891-907. ScholarBank@NUS Repository. https://doi.org/10.1007/s10845-009-0365-8
Abstract: With highly fragmented market and increased competition, platform-based product family design has been recognized as an effective method to construct a product line that satisfies diverse customer's demands while aiming to keep design and production cost-effective. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss. In this paper, a systematic multi-platforming product family approach is proposed to design a scale-based product family. In the light of the basic premise that increased commonality implies enhanced manufacturing efficiency, we present an effective platform decision strategy to quantify family design configuration using a commonality index that couples design varieties with production variation. Meanwhile, unlikemany existingmethods that assume a single given platform configuration, the proposed method addresses the multi-platforming configuration across the family, and can generate alternative product family solutions with different levels of commonality. A modified genetic algorithm is developed to solve the aggregated multiobjective optimization problem and an industrial example of a planetary gear train for drills is given to demonstrate the proposed method. © Springer Science+Business Media, LLC 2009.
Source Title: Journal of Intelligent Manufacturing
URI: http://scholarbank.nus.edu.sg/handle/10635/50816
ISSN: 09565515
DOI: 10.1007/s10845-009-0365-8
Appears in Collections:Staff Publications

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