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TOWARDS A CONFIGURATION MANAGEMENT INTEGRATION TO FEATURE MODELS IN MODEL-BASED PRODUCT LINE ENGINEERING

Published online by Cambridge University Press:  19 June 2023

José Lameh*
Affiliation:
Université Paris Saclay - CentraleSupélec, Laboratoire Genie Industriel, Gif-sur-Yvette, France; Renault, Technocentre, 1 Av. du Golf 78288 Guyancourt, France
Alexandra Dubray
Affiliation:
Renault, Technocentre, 1 Av. du Golf 78288 Guyancourt, France
Marija Jankovic
Affiliation:
Université Paris Saclay - CentraleSupélec, Laboratoire Genie Industriel, Gif-sur-Yvette, France;
*
Lameh, José, CentraleSupélec Université Paris Saclay, France, joselameh@hotmail.com

Abstract

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In parallel with the industry 4.0 revolution, customers’ demand and number of product requirements are increasing, inducing increased product variability. System complexity is growing with the new technologies and system architectures. Hence, maintaining a system consistent and in the desired state becomes crucial. This paper tackles two problems: a product's variability and its temporal evolution. Regarding variability, Model Based Systems Engineering (MBSE) methods and Product Line Engineering (PLE) techniques become essential to configuring products by selecting and arranging features in a combination. In parallel, versioning and temporal evolution are managed with the configuration management (CM) principles. A versioned feature model (FM) is proposed. In PLE methodology, the first step is to model variability through traditional FM. This research presents an extension of the FM by integrating CM into it. The versioned FM includes not only features’ variants but also their versions, making it evolve in time. This work is presented as a base for a work that will study the application of CM for PLE. This study is done in an automotive industry context at Renault Group, and the model proposed is applied to Renault's systems.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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