Date of Award

5-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Industrial Engineering

Committee Chair/Advisor

Tugce Isik

Committee Member

Mary Elizabeth Kurz

Committee Member

Amin Khademi

Committee Member

Emily Tucker

Abstract

This dissertation investigates the optimization of worker allocation in healthcare systems, focusing on flexible staffing models and the strategic prioritization of healthcare tasks. This research explores the dynamics among pre-operative, operative, and post-operative care units under various cost and service-rate constraints, using a series of models that represent realistic healthcare scenarios within a comprehensive framework for improving patient flow and reducing waiting costs.

In Chapter 2, we model and analyze cross-trained nurse allocation policies within an inpatient surgical system. We model the surgical system as a tandem clearing queueing system and formulate Markov decision processes under different business rules governing the movements of servers (i.e., nurses) and jobs (i.e., patients). In Chapter 3, we focus on the theoretical analysis of the immediate recovery model, which stands out from the flexible models introduced in Chapter 2. We characterize the optimal policies starting from a small model and expanding to models with more patients. We further design four heuristics to approximate the optimal policy and minimize the total cost. Chapter 4 broadens the study by analyzing flexible structures for an outpatient system with continuity of care constraints. This model highlights the importance of nurses providing continuous care to the same patient.

This dissertation aims to bridge theoretical modeling with practical applications in healthcare, contributing to the field of operations research in healthcare management. The findings presented offer valuable guidelines for decision-makers, suggesting ways to optimize staff utilization and patient care in inpatient and outpatient systems.

Author ORCID Identifier

0009-0009-2202-2191

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