Year: 2024
Type: conference-paper
Abstract: Biomanufacturing plays an important role in supporting public health and the growth of the bioeconomy. Modeling and studying the interaction effects among various input variables is very critical for obtaining a scientific understanding and process specification in biomanufacturing. In this paper, we use the Shapley-Owen indices to measure the interaction effects for the policy-augmented Bayesian network (PABN) model, which characterizes the risk-and science-based understanding of production bioprocess mechanisms. In order to facilitate efficient interaction effect quantification, we propose a sampling-based simulation estimation framework. In addition, to further improve the computational efficiency, we develop a non-nested simulation algorithm with sequential sampling, which can dynamically allocate the simulation budget to the interactions with high uncertainty and therefore estimate the interaction effects more accurately under a total fixed budget setting.
Source: Winter Simulation Conference
Authors: Junkai Zhao, Jun Luo, Wei Xie, Zixuan Bai
Institutions: Shanghai Jiao Tong University, Northeastern University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Awards: G150567329, G6109992774, G911796368
OpenAlex ID: W4406611608