Different solving strategies on PBO Problems from automotive industry

SAT solvers have proved to be very efficient in verifying the correctness of automotive product documentations. However, in many applications a car configuration has to be optimized with respect to a given objective function prioritizing the selectable product components. Typical applications include the generation of predictive configurations for production planning and the reconfiguration of non-constructible customer orders. So far, the successful application of core guided MaxSAT solvers and ILP-based solvers like CPLEX have been described in literature. In this paper, we consider the linear search performed by DPLL-based PBO solvers as a third solution approach. The aim is to understand the capabilities of each of the three approaches and to identify the most suitable approach for different application cases. Therefore we investigate real-world benchmarks which we derived from the product description of a major German premium car manufacturer. Results show that under certain circumstances DPLL-based PBO solvers are clearly the better alternative to the two other approaches.