Stochastic simulation uncertainty analysis to accelerate flexible biomanufacturing process development

Wei Xie*, Russell R. Barton, Barry L. Nelson, Keqi Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we propose a general metamodel-assisted stochastic simulation uncertainty analysis framework to accelerate the development of a simulation model with modular design for flexible production processes. There are often very limited process observations. Thus, there exist both simulation and model uncertainties in the system performance estimates. In biopharmaceutical manufacturing, model uncertainty often dominates. The proposed framework can produce a confidence interval that accounts for simulation and model uncertainties by using a metamodel-assisted bootstrapping approach. Furthermore, a variance decomposition is utilized to estimate the relative contributions from each source of model uncertainty, as well as simulation uncertainty. This information can be used to improve the system mean performance estimation. Asymptotic analysis provides theoretical support for our approach, while the empirical study demonstrates that it has good finite-sample performance.

Original languageEnglish (US)
Pages (from-to)238-248
Number of pages11
JournalEuropean Journal of Operational Research
Volume310
Issue number1
DOIs
StatePublished - Oct 1 2023

Keywords

  • Biomanufacturing systems
  • Gaussian process (GP)
  • Hybrid simulation model
  • Sensitivity analysis (SA)
  • Uncertainty quantification (UQ)

ASJC Scopus subject areas

  • Information Systems and Management
  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research

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