An Experience Report on the Optimization of the Product Configuration System of Renault

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Abstract

The problem of configuring a variability model is widespread in many different domains. A leading automobile manufacturer has developed its technology internally to model vehicle diversity. This technology relies on the approach known as knowledge compilation to explore the configurations space. However, the growing variability and complexity of the vehicles' range hardens the space representation problem and impacts performance requirements. This paper tackles these issues by exploiting symmetries that represent isomorphic parts in the configurations space. A new method describes how these symmetries are exploited and integrated. The extensive experiments we conducted on datasets from the automobile manufacturer show our approach's robustness and effectiveness: the achieved gain is a reduction of 52.13% in space representation and 49.81% in processing time on average


Bibtex (lrde.bib)

@Proceedings{	  xu.23.iceccs,
  author	= {Hao Xu and Souheib Baarir and Tewfik Ziadi and Siham
		  Essodaigui, Yves Bossu and Lom Messan Hillah},
  title		= {An Experience Report on the Optimization of the Product
		  Configuration System of Renault},
  booktitle	= {26th International Conference on Engineering of Complex
		  Computer Systems},
  publisher	= {IEEE},
  year		= {2023},
  month		= jun,
  abstract	= {The problem of configuring a variability model is
		  widespread in many different domains. A leading automobile
		  manufacturer has developed its technology internally to
		  model vehicle diversity. This technology relies on the
		  approach known as knowledge compilation to explore the
		  configurations space. However, the growing variability and
		  complexity of the vehicles' range hardens the space
		  representation problem and impacts performance
		  requirements. This paper tackles these issues by exploiting
		  symmetries that represent isomorphic parts in the
		  configurations space. A new method describes how these
		  symmetries are exploited and integrated. The extensive
		  experiments we conducted on datasets from the automobile
		  manufacturer show our approach's robustness and
		  effectiveness: the achieved gain is a reduction of 52.13\%
		  in space representation and 49.81\% in processing time on
		  average },
  note		= {To Appear}
}