Abstract
The rise of systems complexity in the automotive industry is one of the big challenges in performing Product Line Engineering (PLE), product customization, and trade study analysis. Model-Based PLE (MBPLE) is useful to overcome these problems. According to Rolling Stock Second Generation PLE (2GPLE), an increasing number of possible features in a feature model and quantity of variation points in a system model can increase the dimension of solution space. With a genetic algorithm (GA), MBPLE can be mutually extended to perform an automation in component selection. The results are divided into 2 sections: (1) perform the MBPLE model transformation based on the feature model from 150%-logical-system-architecture model to 100%-logical-system-architecture model then (2) apply GA to the transformed 100%-logical-system-architecture model and a set of selected components to produce the best solution(s) that could satisfy targeted requirements and weighting factors on multiple objectives. This study can be applied in a different context with pre-defined fitness constraints by domain experts.
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Arifin, H.H., Robert Ong, H.K., Dai, J., Daphne, W., Chimplee, N. (2021). Model-Based Product Line Engineering with Genetic Algorithms for Automated Component Selection. In: Krob, D., Li, L., Yao, J., Zhang, H., Zhang, X. (eds) Complex Systems Design & Management . Springer, Cham. https://doi.org/10.1007/978-3-030-73539-5_23
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