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Stochastic simulation uncertainty analysis to accelerate flexible biomanufacturing process development

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

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.

Suggested Citation

  • Xie, Wei & Barton, Russell R. & Nelson, Barry L. & Wang, Keqi, 2023. "Stochastic simulation uncertainty analysis to accelerate flexible biomanufacturing process development," European Journal of Operational Research, Elsevier, vol. 310(1), pages 238-248.
  • Handle: RePEc:eee:ejores:v:310:y:2023:i:1:p:238-248
    DOI: 10.1016/j.ejor.2023.01.055
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    References listed on IDEAS

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    1. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.
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    6. Corlu, Canan G. & Akcay, Alp & Xie, Wei, 2020. "Stochastic simulation under input uncertainty: A Review," Operations Research Perspectives, Elsevier, vol. 7(C).
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