Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach - Université Paris 1 Panthéon-Sorbonne Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach

Résumé

Among the many product line analysis operations, the computation of material needs for the production of reusable components is one of the most challenging issues. This paper aims at an automatic forecasting of reusable components procurement starting from a product line model. The proposed approach exploits Bayesian networks produced from product line models. The approach is applied on a case study developed at a motor company. Results show effectiveness of the proposed approach while scalability has not yet been reached.
Fichier non déposé

Dates et versions

hal-00913810 , version 1 (04-12-2013)

Identifiants

  • HAL Id : hal-00913810 , version 1

Citer

Raúl Mazo, Gloria Lucia Giraldo, Leon Jaramillo, Camille Salinesi, Cosmin Dumitrescu. Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach. 25th International Conference on Software and Systems Engineering and their Applications (ICSSEA), Nov 2013, Paris, France. ⟨hal-00913810⟩

Collections

UNIV-PARIS1 CRI
53 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More