Biomanufacturing methods use live cells, such as bacteria or insect cells, to manufacture vaccines and proteins. The use of live cells introduces several operational challenges, including uncertainty in yield and quality, random batch failures, and challenges in meeting specific customer requirements for engineer-to-order drugs. In this talk, we present models to reduce costs and lead times in biomanufacturing. Our models are developed and tested in close collaboration with industry.
In the first part of the talk, we present a stochastic model that balances the risk of batch failures and yield/quality trade-offs to reduce costs in upstream biomanufacturing operations. We develop reliability models for random batch failures, and then provide an infinite horizon Markov decision model to derive the structural properties of the optimal operating policies.
In the second part, we analyze a protein purification problem. In this setting, each order denotes an engineer-to-order protein having specific customer requirements on yield and quality. We develop a Markov decision model to optimize purification operations. We partition the state space into distinct decision zones, and provide managerial insights for the optimal operating policies. We develop guidelines that quantify risks and costs. Subsequently, we present a state aggregation and an action elimination scheme leading to computational advantage in solving industry problems.
Brief Bio: Tugce Martagan is a Ph.D. Candidate in Industrial and Systems Engineering at the University of Wisconsin-Madison. Her research interests include stochastic optimization, Markov decision processes, and queuing systems, with applications in operations and supply chain management, and biomanufacturing operations. At the University of Wisconsin-Madison, she has been working at the Center for Quick Response Manufacturing, where she has co-supervised several industry projects, including companies, such as, John Deere, Hy-Vee, and Trek Bicycle Corporation.