This thesis addresses the growing challenge of managing the variability modeling problem (VMP) in Renault's automotive product lines. The automotive industry, particularly at Renault, faces significant combinatorial complexity, as each vehicle can be configured with various options, features, and technical specifications. To illustrate the scale of this diversity, consider the example of the "Master 2" vehicle model. This heavy truck model can be configured in 10^21 different ways. Efficiently managing real-time requests regarding these configurations is the central problem to solve when dealing with such large-scale vehicle models. These requests come from multiple internal departments (e.g., supply chain, manufacturing, and marketing) as well as external customers who need to configure vehicles based on available options.
While it is necessary to respond quickly to these requests, enumerating all possible variants is computationally impractical due to the enormous configuration space. This makes managing such combinatorial diversity both a technical and operational challenge. Theoretically, this problem boils down to solving a constraint satisfaction problem (CSP).
Renault uses a knowledge compilation approach to manage product configuration. This approach precomputes the entire configuration space and stores it in a symbolic structure, allowing rapid query responses by searching within this precompiled space. This method avoids repeatedly solving NP-complete problems but introduces significant memory constraints, as the configuration space can occupy several gigabytes of memory.
However, with the increasing variability of vehicle models, memory requirements for these compiled structures have become problematic. Some models occupy up to 800 MB each, while operational systems handle multiple models simultaneously, leading to memory usage of several tens of gigabytes, putting pressure on system resources. The objective of this thesis is to optimize Renault's current configuration system by reducing the size of the configuration space without compromising response times. Specifically, two main contributions are proposed: