Experimental Design and Analysis for Decision Making: Methodology and Applications

Author

Yezhuo LiFollow

Date of Award

8-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mathematical Sciences

Committee Chair/Advisor

Qiong Zhang

Committee Member

Whitney Huang

Committee Member

Deborah Kunkel

Committee Member

Christopher McMahan

Abstract

This dissertation develops and applies advanced statistical and optimization frameworks to enhance decision-making under uncertainty, particularly in engineering and manufacturing contexts. First, we introduce an approach for the optimal design of controlled experiments that accounts for observational covariates, enabling more precise and personalized decisions. Second, we explore the application of constrained Bayesian optimization, using Gaussian process surrogate models, to optimize composite cure processes, significantly reducing computational effort while maintaining high predictive accuracy. Building on this foundation, we extend Bayesian optimization to bivariate Gaussian process models that capture correlations between objective and constraint functions, offering new insights into multidimensional decision landscapes. Finally, we propose a decision uncertainty quantification framework that integrates polynomial surrogates for simple cases and Gaussian process modeling for complex cases to estimate the variability in optimal decisions robustly.

We validate these methodologies through case studies on cure process optimization and injection molding, demonstrating notable improvements in accuracy and efficiency over traditional approaches. The results indicate that these methods are well suited for larger-scale and more complex applications where systematic exploration of high-dimensional design spaces is required.

Beyond theoretical insights, this work provides a practical roadmap for data-driven optimization in real-world settings. It highlights the value of personalized experimentation, robust surrogate modeling, and uncertainty-aware decision strategies. Future directions include expanding these frameworks to multi-objective problems, exploring non-separable covariance functions in Gaussian process models, and scaling to even higher-dimensional domains. Ultimately, the techniques presented in this dissertation aim to bridge the gap between rigorous statistical theory and complex industrial challenges, offering versatile tools for researchers and practitioners alike.

Recommended Citation

Li, Yezhuo, "Experimental Design and Analysis for Decision Making: Methodology and Applications" (2025). All Dissertations. 4046.
https://open.clemson.edu/all_dissertations/4046

Author ORCID Identifier

0009-0000-9468-3735