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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/18003

Title: Multi-objective grouping genetic algorithm for product life-cycle optimisation
Authors: Lee, Michael
Case, Keith
Marshall, Russell
Keywords: Genetic algorithms
Life cycle design
Multi- criteria decision making
Optimization
Modularity
Issue Date: 2016
Publisher: Engg Journals Publication
Citation: 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, forthcoming.
Abstract: A 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.
Description: This paper is closed access until publication.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/18003
Publisher Link: http://www.ijest.info/
ISSN: 2278-9510
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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