Efficient Solution of Enterprise-Wide Optimization Problems Using Nested Stochastic Blockmodeling

Enterprise-wide optimization seeks to improve the economic performance of process systems by considering simultaneously decisions at different time scales, resulting in large-scale optimization problems. In this paper, we propose the application of nested stochastic blockmodeling (nSBM) for the decomposition of such optimization problems. This approach allows the identification of the block structure of the problem at different hierarchical levels and the hierarchy itself. We consider problems of integration of scheduling and dynamic optimization and integration of planning, scheduling, and dynamic optimization for illustration. Application of nSBM reveals the multiscale nature of these optimization problems, and the exploitation of the structure of the problem at different hierarchical levels enables efficient solutions.