A stochastic optimization model to improve production planning and R&D resource allocation in biopharmaceutical production processes

The increasing cost of health care has brought pressure to reduce pharmaceutical costs, and because manufacturing and R&D are significant cost factors, these areas have been targeted as potential sources of cost reduction. Manufacturing costs are particularly high in the biotechnology industry because process technologies are relatively new. Contamination, genetic instability, and other factors complicate production planning and make bioprocess systems unreliable. This paper presents a Markov decision process model that combines features of engineering design models and aggregate production planning models to obtain a hybrid model that links biological and engineering parameters to optimize operations performance. Using tissue plasminogen activator as a specific example, the paper shows how the hybrid modeling approach not only improves production planning, but also provides accurate information on the operating performance of bioprocesses that can be used to predict the financial impact of process changes. Therefore, the model can be used to guide investments in manufacturing process improvement and R&D (e.g., genetic modifications). Although stochastic production models are not commonly used in process design, this paper shows how a combined engineering production model can facilitate a concurrent design approach to reduce cost in bioprocess development.