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Download fileMulti-objective grouping genetic algorithm for product life-cycle optimisation
journal contribution
posted on 2015-06-24, 11:15 authored by Michael J. Lee, Keith Case, Russell MarshallRussell MarshallA product’s lifecycle performance (e.g. assembly, outsourcing, maintenance and recycling) can often be improved through modularity. However, modularisation under different and often conflicting lifecycle objectives is a complex problem that will ultimately require trade-offs. This paper presents a novel multi-objective modularity optimisation framework; the application of which is illustrated through the modularisation of a car climate control system. Central to the framework is a specially designed multi-objective grouping genetic algorithm (MOGGA) that is able to generate a whole range of alternative product modularisations. Scenario analysis, using the principles of the analytical hierarchical process (AHP), is then carried out to explore the solution set and choose a suitable modular architecture that optimises the product lifecycle according to the company’s strategic vision.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
International Journal of Engineering Science and TechnologyVolume
11Issue
1Pages
1 - 17Citation
LEE, M., CASE, K. and MARSHALL, R., 2016. Multi-objective grouping genetic algorithm for product life-cycle optimisation. International Journal of Engineering Science and Technology, 11 (1), pp.1-17.Publisher
Engg Journals PublicationVersion
- AM (Accepted Manuscript)
Rights holder
© School of Engineering, Taylor’s UniversityPublisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2016ISSN
2278-9510eISSN
0975-5462Publisher version
Language
- en