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An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality
| Content Provider | Semantic Scholar |
|---|---|
| Author | Khajavirad, Aida |
| Copyright Year | 2009 |
| Abstract | Product family optimization involves not only specifying the platform from which the individual product variants will be derived, but also optimizing the platform design and the individual variants. Typically these steps are performed separately, but we propose an efficient decomposed multiobjective genetic algorithm to jointly determine optimal (1) platform selection, (2) platform design, and (3) variant design in product family optimization. The approach addresses limitations of prior restrictive component sharing definitions by introducing a generalized two-dimensional commonality chromosome to enable sharing components among subsets of variants. To solve the resulting high dimensional problem in a single stage efficiently, we exploit the problem structure by decomposing it into a two-level genetic algorithm, where the upper level determines the optimal platform configuration while each lower level optimizes one of the individual variants. The deA preliminary version of this paper was presented at the 2007 AIAA Multidisciplinary Design Optimization Specialists Conference. A. Khajavirad Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA e-mail: aida@cmu.edu J. J. Michalek (B) Mechanical Engineering and Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA e-mail: jmichalek@cmu.edu T. W. Simpson Mechanical and Industrial Engineering, The Pennsylvania State University, University Park, PA 16802, USA e-mail: tws8@psu.edu composed approach improves scalability of the all-inone problem dramatically, providing a practical tool for optimizing families with more variants. The proposed approach is demonstrated by optimizing a family of electric motors. Results indicate that (1) decomposition results in improved solutions under comparable computational cost and (2) generalized commonality produces families with increased component sharing under the same level of performance. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cmu.edu/me/ddl/publications/2009-SMO-Khajavirad-Michalek-Simpson-Product-Family-GA.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Addresses (publication format) Algorithmic efficiency Computation Electroconvulsive Therapy Email Genetic algorithm Industrial engineering Mathematical optimization Multidisciplinary design optimization Scalability Simpson's rule Solutions |
| Content Type | Text |
| Resource Type | Article |
