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Automated Parameter Determination for Enhancing the Product Configuration System of Renault: An Experience Report

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Engineering of Complex Computer Systems (ICECCS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14784 ))

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Abstract

The problem of configuring the variability models is pervasive in plenty of domains. Renault, a leading automobile manufacturer, has developed an internal product configuration system to model its vehicle diversity. This system is based on the well-known knowledge compilation approach and is associated with a set of parameters. Different input parameters have a strong influence on the system’s performance. The parameters actually used are determined manually. Our work aims to study and determine these parameters automatically. This paper studies Renault’s variability models and product configuration system and presents a parameter prediction model for this system. The results show the predicted parameters’ competitiveness compared with the parameters by default.

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Notes

  1. 1.

    Here, “compilation” refers to the process of building the configuration space in knowledge compilation terminology.

  2. 2.

    The daily parameter is also called the production parameter, which is manually determined and updated by the system developers.

  3. 3.

    The degree of a vertex in an undirected graph is the number of edges incident with (meeting at or ending at) itself [16].

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Author information

Authors and Affiliations

  1. LIP6, Paris, France

    Hao Xu, Souheib Baarir & Tewfik Ziadi

  2. Paris Nanterre University, Paris, France

    Souheib Baarir

  3. Renault Group, Paris, France

    Hao Xu, Siham Essodaigui & Yves Bossu

Authors
  1. Hao Xu
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  2. Souheib Baarir
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  3. Tewfik Ziadi
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  4. Siham Essodaigui
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  5. Yves Bossu
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Corresponding author

Correspondence to Hao Xu .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia

    Guangdong Bai

  2. Information Systems Architecture Science Research Division, National Institute of Informatics, Chiyoda-ku, Tokyo, Japan

    Fuyuki Ishikawa

  3. IRIT, University of Toulouse, Toulouse, France

    Yamine Ait-Ameur

  4. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

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Xu, H., Baarir, S., Ziadi, T., Essodaigui, S., Bossu, Y. (2025). Automated Parameter Determination for Enhancing the Product Configuration System of Renault: An Experience Report. In: Bai, G., Ishikawa, F., Ait-Ameur, Y., Papadopoulos, G.A. (eds) Engineering of Complex Computer Systems. ICECCS 2024. Lecture Notes in Computer Science, vol 14784 . Springer, Cham. https://doi.org/10.1007/978-3-031-66456-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-66456-4_3

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