Integration of scheduling and dynamic optimization for sequential batch processes

We propose an efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes. The integrated problem is formulated as a mixed-integer dynamic optimization problem. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem. Information of the dynamic models is encapsulated by a flexible recipe which is characterized by Pareto frontiers. The Pareto frontiers are determined offline by using multi-objective dynamic optimization to minimize the processing cost and processing time. The flexible recipe is then optimized simultaneously with the scheduling decisions online. After the decomposition, the online problem is a mixed integer linear programming problem which is computationally efficient and allows the online implementation.