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Model-Based Product Line Engineering with Genetic Algorithms for Automated Component Selection

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Complex Systems Design & Management

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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Author information

Authors and Affiliations

  1. Dassault Systèmes, Vélizy-Villacoublay, France

    Habibi Husain Arifin, Ho Kit Robert Ong, Wu Daphne & Nasis Chimplee

  2. Vision MC, Shanghai, China

    Jie Dai

Authors
  1. Habibi Husain Arifin
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  2. Ho Kit Robert Ong
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  3. Jie Dai
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  4. Wu Daphne
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  5. Nasis Chimplee
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Corresponding author

Correspondence to Habibi Husain Arifin .

Editor information

Editors and Affiliations

  1. CESAMES, Paris, France

    Daniel Krob

  2. Tsinhua University, Beijing, China

    Lefei Li

  3. Chinese Society for Aeronautics, Beijing, China

    Junchen Yao

  4. CSSC Systems Engineering Research Institute, Beijing, China

    Hongjun Zhang

  5. Tsinhua University, Beijing, China

    Xinguo Zhang

Appendices

Appendices

See appendix Tables 3, 4, 5, 6, and 7; Figs. 11 and 12.

Table 3 GA terms and concepts [13, 21, 22, 25 ,33–36]
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Table 4 GA component selection terms and concepts
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Table 5 Table of components of used example (car)
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Table 6 Table of structure model from 100% Model
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Table 7 Table of instances (bill of materials)
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Fig. 11
figure 11

Completed 150% model

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Fig. 12
figure 12

Example of 100% model of StandardCar variant

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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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  • DOI: https://doi.org/10.1007/978-3-030-73539-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73538-8

  • Online ISBN: 978-3-030-73539-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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