In this project, we will develop optimization models and decision support tools to reduce production lead times and costs in continuous biomanufacturing systems. These models will combine the knowledge from life sciences and industrial engineering (e.g., stochastic models, and robust optimization) to design and optimize continuous biomanufacturing operations under uncertainty and variability. For this purpose, we will use both predictive and prescriptive analytics. Existing research on continuous biomanufacturing mostly focuses on the underlying biological and chemical dynamics of these processes (cell-level aspects). However, in practice, we also need to account for system-level aspects, such as, financial implications (i.e., operating costs) and business risks (i.e., uncertainty in quality, yield and lead times) to achieve the maximum efficiency. Therefore, our project will adopt a unique approach that accounts for both cell-level and system-level dynamics to control and optimize continuous biomanufacturing systems. By developing novel models and decision support tools which are custom-designed for MSD, we aim to • reduce production costs and lead times, • increase production yield and quality, • increase throughput without making investments on additional capacity, and • obtain the highest manufacturing efficiency, robustness and flexibility.