Date of Award
8-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Industrial Engineering
Committee Chair/Advisor
Dr. Sudeep Hegde
Committee Member
Dr. Kevin Taaffe
Committee Member
Dr. Carl Ehrett
Committee Member
Dr. Kenneth Catchpole
Abstract
Complex systems often depend on adaptive performance that is difficult to capture using structured data or formal models alone. This dissertation asks how predictive modeling, coordination-cost measurement, and simulation can be integrated to better represent the visible, hidden, and unrepresented dynamics that shape real-world system performance.
Sterile Processing Departments (SPDs) provide the empirical context for this work because they support safe and timely surgical care while operating under high workload variability, time pressure, and tight coordination with operating rooms. Hospitals rely on SPDs to clean, assemble, sterilize, and deliver surgical instrument trays so surgeries can start on time and proceed safely. When SPD work runs smoothly, much of the coordination behind it is invisible. However, when an instrument is missing, a tray is incomplete, or demand is higher than expected, SPD and operating room teams must coordinate quickly under pressure.
Using SPD operations as the focal case, this dissertation develops an integrated framework for connecting what can be predicted from data with how people adapt in real work. The dissertation uses a three-layer view of system behavior: represented dynamics, hidden dynamics, and unrepresented dynamics. First, this dissertation explores machine-learning models to forecast daily surgical tray demand by service line, providing SPD leaders with a workload planning signal for staffing, tray staging, and capacity preparation. Second, it proposes an entropy-based coordination cost framework to measure hidden coordination work during missing-instrument events, including communication burden, handoffs, retries, and pathway uncertainty. Third, it builds an agent-based simulation model to evaluate how different response strategies perform under changing demand, staffing, forecast mismatch, resource availability, coordination knowledge, and structured versus ad-hoc response pathways.
Overall, this dissertation shows that resilient system performance requires more than accurate forecasts or fast response strategies alone. By combining forecasting, coordination-cost measurement, practitioner-informed model refinement, and simulation, this work supports more adaptive, coordination-aware decision support for sterile processing and other complex sociotechnical systems.
Recommended Citation
Liu, Dixizi, "Bridging Models and Real-World Performance: Capturing Represented, Hidden, and Unrepresented Dynamics in Complex Systems" (2026). All Dissertations. 4299.
https://open.clemson.edu/all_dissertations/4299
Author ORCID Identifier
0009-0004-2422-5463
Included in
Health Services Research Commons, Industrial Engineering Commons, Operational Research Commons, Systems Engineering Commons