Biomanufacturing methods use living organisms (i.e., viruses and bacteria) to generate active ingredients, and this leads to challenges that are different to those incurred by other industries. For example, biomanufacturers often deal with high levels of uncertainty and batch-to-batch variability in production yield, lead times, and costs. Bio-safety requirements impose constraints, such as a no-wait requirement throughout the production process. In addition, biomanufacturing operations are cost and labor intensive, and involve high risks of failure. To address these challenges, a multidisciplinary approach was adopted to develop a portfolio of optimization models and decision support tools. These tools were aimed at improving biomanufacturing efficiency using a variety of operations research methodologies, including stochastic optimization, Bayesian design of experiments, and simulation-optimization. The developed models link the underlying biology and chemistry of biomanufacturing processes with financial trade-offs and business risks.
The research has been conducted in close collaboration with Merck Sharp & Dohme Animal Health (MSD AH) in Boxmeer, the Netherlands. Industry implementation at MSD AH had a significant impact with up to 50% increase in batch yield and an additional revenue of 50 million Euro per year. The application of operations research is very new to the biomanufacturing industry. As more companies such as MSD AH embrace operations research, we believe that this will significantly help the industry provide faster and more affordable access to new treatments.
Operations Research Improves Biomanufacturing Efficiency [Click here].
Optimal Production Decisions in Biopharmaceutical Fill-and-Finish Operations [Click here].
Managing Trade-offs in Protein Manufacturing [Click here].
Increasing Biomanufacturing Yield with Bleed-Feed [Click here].
Performance Guarantees and Optimal Purification Policies [Click here].
Stochastic simulation model development for biopharmaceutical production [Click here].
Simulation-based production planning with random yield [Click here].
Optimizing Biomanufacturing Harvesting Decisions under Limited Historical Data.
The project combines the foundations of Operations Research, machine learning, simulation-optimization, chemical and biological engineering, and biomanufacturing. A multi-disciplinary team of researchers were involved in the execution of the project while transferring the knowledge from theory to real-world practice (i.e., upper and middle management, process improvement engineers, and scientists working on daily production processes in clean rooms).
The developed tools and models address common problems encountered in the industry (not only specific to MSD). We believe that the research outcomes can be also generalized to other industries, such as the food industry (e.g., operations that involve fermentation, for example, yogurt making, beer and wine processing, etc).
During my Ph.D. at the University of Wisconsin-Madison, I have worked with several local biomanufacturers, and applied various Operations Research methodologies to improve operating decisions. One example project is shown in the video below.