A surrogate-based framework for feasibility-driven optimization of expensive simulations

Surrogate-based optimization approaches have been widely adopted in industrial problems due to their potential to reduce the number of simulation runs required in the optimization process. The surrogate-based optimization framework has been extended to feasibility analysis in pharmaceutical manufacturing to characterize the design space. Most surrogate-based approaches for feasibility analysis are limited to the construction of a regression model for the feasibility function. In this work, we developed a framework with the feasibility problem considered as a classification problem, and additional stages introduced to improve local exploitation and global exploration. We illustrate the efficiency of the proposed framework on three test problems and implement it in a realistic case study describing the production of solid-based drugs using wet granulation, aimed to reduce the operation cost, improve product quality, and increase process flexibility and robustness.