Data-Driven Robust Optimization in Healthcare Applications

abstract: Healthcare operations have enjoyed reduced costs, improved patient safety, and innovation in healthcare policy over a huge variety of applications by tackling prob- lems via the creation and optimization of descriptive mathematical models to guide decision-making. Despite these accomplishments, models are stylized representations of real-world applications, reliant on accurate estimations from historical data to jus- tify their underlying assumptions. To protect against unreliable estimations which can adversely affect the decisions generated from applications dependent on fully- realized models, techniques that are robust against misspecications are utilized while still making use of incoming data for learning. Hence, new robust techniques are ap- plied that (1) allow for the decision-maker to express a spectrum of pessimism against model uncertainties while (2) still utilizing incoming data for learning. Two main ap- plications are investigated with respect to these goals, the first being a percentile optimization technique with respect to a multi-class queueing system for application in hospital Emergency Departments. The second studies the use of robust forecasting techniques in improving developing countries’ vaccine supply chains via (1) an inno- vative outside of cold chain policy and (2) a district-managed approach to inventory control. Both of these research application areas utilize data-driven approaches that feature learning and pessimism-controlled robustness.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201