A Hybrid Approach Combining Simulation and a Queueing Model for Optimizing a Biomanufacturing System

We explore a hybrid approach to designing a biomanufacturing system with low-volume, high variability, and individualized products. Simulating a large number of possible configurations to determine those that meet target production goals is computationally impractical. We create an explainable surrogate model, specifically a queueing network model, that is calibrated to the output of a few computationally expensive simulations. The queueing network model enables a quick exploration of large numbers of mixed integer-continuous configurations, which would be challenging for traditional surrogate-based approaches. The queueing network model is used to quickly identify promising regions where a few configurations can then be evaluated with the simulation. The difference in evaluations at these configurations is used to decide whether the queueing model requires partitioning and/or re-calibration. The use of this hybrid approach with an explainable surrogate enables analysis, such as identifying bottle-necks, and gives insight into robust designs of the biomanufacturing system.