Integration of scheduling and dynamic optimization of batch processes under uncertainty

Integration of scheduling and dynamic optimization significantly improves the overall performance of a production process compared to the traditional sequential method. However, most integrated methods focus on solving deterministic problems without explicitly taking process uncertainty into account. We propose a novel integrated method for sequential batch processes under uncertainty. The integrated problem is formulated into a two-stage stochastic program. To solve the resulting complicated integrated problem, we develop an efficient algorithm based on the framework of generalized Benders decomposition. For a complicated case study with more than 3 million variables/equations under 100 scenarios, the direct solution approach does not find a feasible solution while the decomposition algorithm return the optimal solution in 23.9 hours. The integrated method returns a higher average profit than the sequential method by 17.6%.