This paper reviews recent developments in our project that are focused on dynamic data-driven methods for efficient and reliable simulation based optimization, which may be suitable for a wide range of different application problems. The emphasis in this paper is on the coupling of parallel multiblock predictive models with optimization, the development of autonomic execution engines for distributing the associated computations, and deployment of systems capable of handling large datasets. The integration of these components results in a powerful framework for developing large-scale and complex decision-making systems for dynamic data-driven applications.