Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35811
Title: Linear programming embedded genetic algorithm for product family design optimization with maximizing imprecise part-worth utility function
Authors: Luo, XG
Yang, W
Kwong, CK 
Tang, JG
Tang, JF
Keywords: Product family
Genetic algorithm
Customer preference
Optimization
Imprecision
Issue Date: 2014
Publisher: SAGE Publications
Source: Concurrent engineering research and applications, 2014, v. 22, no. 4, p. 309-319 How to cite?
Journal: Concurrent engineering research and applications 
Abstract: Product family design optimization is an important decision task in the early stages of product development. The extant optimization models for product family design assume that the known information for modeling can be determined precisely. However, many collected information for product design are prone to be imprecise due to the inherent uncertainty of human knowledge and expression. For example, when human experts estimate market demand of a product, imprecise information may be involved and influence the results of optimization. In this research, an optimization model with maximizing imprecise part-worth utility function is established for product family design problem. A linear programming embedded genetic algorithm is proposed to solve the proposed fuzzy optimization model. An industrial case of printing calculator product is used to illustrate the proposed approach. Experiments and sensitivity analysis based on the case study are also performed to analyze the relationship among the parameters and to explore the characteristics of the optimization model.
URI: http://hdl.handle.net/10397/35811
ISSN: 1063-293X (print)
1531-2003 (online)
DOI: 10.1177/1063293X14553068
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