Author ORCID Identifier

https://orcid.org/0000-0002-3434-0164

Date of Award

Spring 5-5-2025

Document Type

Thesis (Ph.D.)

Department or Program

Engineering Sciences

First Advisor

Vikrant Vaze

Second Advisor

Jonathan T. Elliott

Third Advisor

Alexandre Jacquillat

Abstract

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal solution components by leveraging historical data. By integrating binary classification methods into a decision-aware framework, our approach prunes the decision space effectively, yielding high-quality solutions in significantly shorter runtimes than both exact and heuristic methods.

In the healthcare domain, for the diagnosis of occult hemorrhage, we develop a data-driven framework that takes advantage of multisensor data and vital signs to detect internal bleeding early, particularly under capacity constraints. We conduct extensive experiments with animal and human data to engineer high-fidelity features and maximize predictive accuracy. Building on these predictions, we propose a decision-aware machine learning approach that dynamically updates patient risk scores over time and optimizes admissions to resource-intensive care units. Our method balances the trade-off between acting early (with less accurate information) versus waiting for more precise data, thus reducing both false positives and missed diagnoses. Through experiments based on data sets from our preclinical study, we show that our dynamic optimization framework surpasses traditional risk-based heuristics, leading to significantly improved patient outcomes.

Through extensive computational experiments on realistic data sets based on our preclinical study, this thesis demonstrates that our integrated, decision-aware framework not only accelerates online decision-making but also consistently produces near-optimal solutions, offering significant improvements in both airline operations recovery and hemorrhage diagnosis.

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