Solving the joint product platform selection and product family design problem: An efficient decomposed multiobjective genetic algorithm with generalized commonality

Aida Khajavirad, Jeremy J. Michalek, Timothy William Simpson

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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 platform selection, platform design, and 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 decomposed approach improves scalability of the all-in-one 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 decomposition results in improved solutions under comparable computational cost, and generalized commonality produces families with increased component sharing under the same level of performance.

Original languageEnglish (US)
Title of host publicationAdvances in Product Family and Product Platform Design
Subtitle of host publicationMethods and Applications
PublisherSpringer New York
Pages271-294
Number of pages24
ISBN (Electronic)9781461479376
ISBN (Print)9781461479369
DOIs
StatePublished - Jan 1 2014

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Genetic algorithms
Electric motors
Chromosomes
Scalability
Decomposition
Costs

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Khajavirad, A., Michalek, J. J., & Simpson, T. W. (2014). Solving the joint product platform selection and product family design problem: An efficient decomposed multiobjective genetic algorithm with generalized commonality. In Advances in Product Family and Product Platform Design: Methods and Applications (pp. 271-294). Springer New York. https://doi.org/10.1007/978-1-4614-7937-6_11
Khajavirad, Aida ; Michalek, Jeremy J. ; Simpson, Timothy William. / Solving the joint product platform selection and product family design problem : An efficient decomposed multiobjective genetic algorithm with generalized commonality. Advances in Product Family and Product Platform Design: Methods and Applications. Springer New York, 2014. pp. 271-294
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Khajavirad, A, Michalek, JJ & Simpson, TW 2014, Solving the joint product platform selection and product family design problem: An efficient decomposed multiobjective genetic algorithm with generalized commonality. in Advances in Product Family and Product Platform Design: Methods and Applications. Springer New York, pp. 271-294. https://doi.org/10.1007/978-1-4614-7937-6_11

Solving the joint product platform selection and product family design problem : An efficient decomposed multiobjective genetic algorithm with generalized commonality. / Khajavirad, Aida; Michalek, Jeremy J.; Simpson, Timothy William.

Advances in Product Family and Product Platform Design: Methods and Applications. Springer New York, 2014. p. 271-294.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Khajavirad A, Michalek JJ, Simpson TW. Solving the joint product platform selection and product family design problem: An efficient decomposed multiobjective genetic algorithm with generalized commonality. In Advances in Product Family and Product Platform Design: Methods and Applications. Springer New York. 2014. p. 271-294 https://doi.org/10.1007/978-1-4614-7937-6_11