Product lifecycle optimisation of car climate controls using analytical hierarchical process (Ahp) analysis and a multi-objective grouping genetic algorithm (mogga)
journal contributionposted on 11.02.2016 by Michael J. Lee, Keith Case, Russell Marshall
Any type of content formally published in an academic journal, usually following a peer-review process.
© School of Engineering, Taylor’s University. 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.
- Mechanical, Electrical and Manufacturing Engineering