Doctoral Dissertations

Keywords and Phrases

Artificial Intelligence; Complex Adaptive Systems; Deep Learning; Genetic Algorithm; Kidney Allocation; Optimization

Abstract

"The kidney allocation system is a complex, evolving system involving multiple heterogeneous agents. Each agent exhibits emergent behavior that may not fully align with the complex system goals. Therefore, there is a need for a transdisciplinary systems approach to visualize the interdependency among agents and understand the dominant patterns that shape the kidney allocation system.

First, this research presented an incremental hierarchical system engineering approach in identifying the agents’ needs and behaviors toward the complex systems’ goal of maximizing deceased donor kidney utilization and reducing kidney discard. The hierarchical systems approach linked with model-based system engineering aided in eliciting agents’ needs, behaviors, boundaries, and interactions.

Next, the research presented AI-enabled decision support systems that address agent practices and decision-making. The decision support system implementation uses deep learning models. Hyperparameter tuning of deep learning models is a challenging manual process; therefore, deep architecture encoding for binary representation was attended. The binary representation formulated a chromosome used in the genetic algorithm to optimize deep architecture hyperparameters. This approach optimized agents’ representations or transformed their multi-perspective toward the complex systems’ goal.

Finally, many-objective optimization models were used to produce optimal and diverse solutions to measure each agent’s performance in increasing the utilization of deceased donor kidneys and maximizing incremental life years gained"--Abstract, p. iii

Advisor(s)

Dagli, Cihan H., 1949-

Committee Member(s)

Canfield, Casey I.
Lentine, Krista
Paige, Robert L.
Corns, Steven

Department(s)

Engineering Management and Systems Engineering

Second Department

Electrical and Computer Engineering

Degree Name

Ph. D. in Systems Engineering

Publisher

Missouri University of Science and Technology

Publication Date

2026

Pagination

xii, 131 pages

Note about bibliography

Includes_bibliographical_references_(pages 120-129)

Rights

© 2023 Lirim Ashiku , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12631

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