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Multi-objective grouping genetic algorithm for product life-cycle optimisation

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journal contribution
posted on 24.06.2015, 11:15 authored by Michael J. Lee, Keith Case, Russell MarshallRussell Marshall
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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

International Journal of Engineering Science and Technology

Volume

11

Issue

1

Pages

1 - 17

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, 11 (1), pp.1-17.

Publisher

Engg Journals Publication

Version

AM (Accepted Manuscript)

Rights holder

© School of Engineering, Taylor’s University

Publisher 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

2016

ISSN

2278-9510

eISSN

0975-5462

Language

en