Abstract

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.

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