Resource-constrained decision and optimization problems in production and logistics are characterized by combinatorial complexity, heterogeneous constraints, and dynamic environments. Traditional declarative modeling offers expressive representations of such problems, but their direct formulations often result in intractable models. Building upon the research on hybrid declarative approaches, this paper presents a data-driven model transformation approach that systematically restructures resource-constrained problems before solution. This approach combines presolving techniques with data-driven analysis of problem structures to detect redundancies, reformulate constraints, and generate solver-efficient models. The transformation process can be applied to representative classes of production scheduling and logistics problems, where experiments confirm significant improvements in computational efficiency and scalability, while preserving solution accuracy. The paper proposes an original production optimization model with a data-driven model transformation approach for a real crayon factory. The results extend earlier work on model-driven problem solving by demonstrating the effectiveness of data-driven transformation strategies.