Operations Research, Systems Engineering and Industrial Engineering Commons^™

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …

Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern, Lauren E. Caruso, Vincent J. Compeau, Assefaw H. Gebremedhin

Military Cyber Affairs

COBOL-based legacy systems continue to underpin critical infrastructure in banking and government sectors despite their age and associated cybersecurity risks. Originally developed through a Department of Defense–sponsored initiative to standardize business computing, COBOL remains widely used in mission-critical environments. However, these systems face increasing vulnerabilities due to outdated security architectures, workforce shortages, and rising maintenance costs. This paper examines cybersecurity and operational challenges associated with COBOL systems and evaluates the Strangler Fig pattern as a modernization strategy that enables incremental replacement while maintaining continuity. The findings highlight implications for financial institutions and public-sector organizations dependent on legacy infrastructure.

Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu

Military Cyber Affairs

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity challenges, particularly for resource-constrained devices that traditional intrusion detection systems often fail to protect effectively. This paper proposes a novel, two-phase autonomous security pipeline designed to bridge the gap between probabilistic threat detection and deterministic network enforcement. The framework first utilizes a custom Time Series Transformer (TST) to classify multivariate network traffic and identify specific attack vectors, such as ransomware, SQL injections, and malicious file uploads. In the second phase, an agentic AI layer, comprising a locally hosted Large Language Model (LLM) orchestrated via LangGraph, processes the …

Improving Cancer Diagnosis And Patient Outcomes With Deep Learning Models, Mariana Arriz-Jorquiera

USF Tampa Graduate Theses and Dissertations

Cancer care depends on timely and reliable decisions, from detection and diagnosis to treatment planning and patient monitoring. These decisions are often made under uncertainty because medical images and healthcare data may be noisy, incomplete, or difficult to interpret. In breast cancer imaging, ultrasound is widely used because it is safe, accessible, and complementary to other imaging modalities. However, variations in image quality, acquisition conditions, and noise can obscure lesion boundaries and texture, affecting human interpretation and artificial intelligence reliability. This dissertation develops deep learning, image-analysis, and optimization methods to improve healthcare decisions under imperfect information. Its primary focus is …

From Shortage To Strategy: Optimizing Scarce Resource Allocation In Healthcare, Daniela Duran Cantarino

USF Tampa Graduate Theses and Dissertations

The allocation of scarce medical resources presents one of the most consequential challenges in health care, requiring decisions that balance equity and efficiency under pervasive uncertainty. This dissertation develops optimization-based frameworks for two critical domains, organ transplantation and emergency medical services, and demonstrates how operations research methods can improve upon current practice along both dimensions simultaneously.

In the domain of deceased-donor kidney allocation, two complementary frameworks are proposed. The first is a bi-objective stochastic optimization model that jointly maximizes post-transplant survival (efficiency) and prioritizes candidates with the highest pre-transplant mortality risk (equity), subject to regional chance constraints on graft failure …

Pathways To Efficient And Equitable Solutions For Large-Scale Routing Problems, Abhay Sobhanan

USF Tampa Graduate Theses and Dissertations

This dissertation addresses large-scale optimization problems in transportation emerging from hierarchical decision-making, equitable workload allocation, and innovative routing logistics. It presents three sets of contributions, each detailed in a separate chapter, and offers computational tools and insights to advance both the theory and practice of transportation systems.

The first work introduces a deep learning-enhanced genetic algorithm framework for solving the Hierarchical Vehicle Routing Problems (HVRPs). Traditional optimization approaches to such problems require extensive evaluation of multiple lower-level routing solutions and are computationally intensive. Our innovative method integrates a genetic algorithm with a pretrained graph neural network, which is trained on …

Integrating Risk And Vulnerability: Exploring A Unified Model For Supply Chain Resiliency, William G. Cook

USF Tampa Graduate Theses and Dissertations

The world has entered an era of retreating globalization, mounting geo-political tensions, rising protectionism, and increasing focus on the fragility of complex supply chains. The negative impacts of supply chain disruptions have been increasingly documented since the turn of the century. Given the global scale of recent disruptions, supply chain resiliency has become a national imperative. The Global Financial Crisis, the Covid-19 pandemic, and other major disruptive events demonstrate the active role of government in mitigating damage, the enduring effects of regulation, and the resultant re-evaluation of supply chain strategies by the private and public sectors. In this environment, supply …

Economic Planning And Operation Of Ev Charging Infrastructure Participating In Electric Power Markets, Diwas Paudel

USF Tampa Graduate Theses and Dissertations

As electric vehicles (EVs) are becoming more common, there are not enough charging infrastructures to keep up with the projected EV charging demands. This necessitates investment from private enterprises in the charging infrastructure, along with additional government investments, to support the expected EV growth. The profit potential of the EV charging business will be crucial in attracting private enterprises to invest in the infrastructure. The profitability of these infrastructures depends on their location and effective operation. The charging infrastructure needs to be strategically placed and sized in the service area of interest. Once built, they need a robust strategy to …

Transforming Organizational Cyber Security With Artificial Intelligence And Data-Driven Optimization, Soumyadeep Hore

USF Tampa Graduate Theses and Dissertations

This dissertation presents a comprehensive framework for enhancing organizational cybersecurity through data-driven intelligence. The research integrates multiple methodologies to tackle challenges in network intrusion detection and vulnerability management within cybersecurity operations centers (CSOCs). First, the research investigates vulnerability prioritization and mitigation techniques currently employed by CSOCs. To further streamline the vulnerability prioritization and mitigation process a machine learning (ML)-based Vulnerability Priority Scoring System (VPSS) is introduced, significantly improving the prioritization and mitigation of context-sensitive vulnerabilities. The VPSS outperforms traditional methods, reducing the cumulative vulnerability exposure score by up to 30% by considering both organizational context and vulnerability severity. Next, the …

A Robust Data-Driven Framework For Artificial Intelligent Systems, Quoc H. Nguyen

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) systems have demonstrated remarkable performance across various domains. However, their robustness remains a critical concern, particularly in terms of data and model reliability. This dissertation aims to address the challenges associated with building robust AI systems by focusing on two key aspects: data robustness and model robustness. Data robustness poses significant challenges, including data shift, concept shifting, limited and imbalanced datasets, and interoperability issues in IoT systems for data collection. Existing methods fall short in handling dynamic business objectives and evolving data landscapes effectively. To bridge these gaps, we propose an IoT framework that ensures interoperability, seamless …

Routing Problems Through The Lens Of Hybrid Algorithms, Sasan Mahmoudinazlou

USF Tampa Graduate Theses and Dissertations

This dissertation explores novel approaches to address complex combinatorial optimization challenges in transportation and routing scenarios. Three sets of contributions are presented, each encapsulated in a chapter. The first set of contributions introduces a pioneering hybrid genetic algorithm meticulously crafted to address the intricacies of the Traveling Salesman Problem with Drone (TSPD) and the Flying Sidekick Traveling Salesman Problem (FSTSP). These emerging problems involve the strategic use of both ground-based trucks and aerial drones for efficient package delivery. Our algorithm stands out by leveraging sophisticated chromosomes and dynamic programming, allowing for broad exploration by the genetic algorithm and effective exploitation …

Knowledge Integration In Domain-Informed Machine Learning And Multi-Scale Modeling Of Nonlinear Dynamics In Complex Systems, Phat K. Huynh

USF Tampa Graduate Theses and Dissertations

Nonlinear dynamical systems have been extensively used to model various phenomena in the changing world around us, especially in science and engineering fields. Thanks to breakthrough advancements in sensing technologies, an increasingly high volume of multi-modal sensor data has been collected, which enables us gain better insights into complex systems dynamics and build sophisticated data-driven machine-learning-based dynamic models without having the access to the underlying governing equations. However, integrating domain-specific knowledge in machine learning algorithms remains pivotal for various reasons: it promises enhanced predictive accuracy, better model interpretability, and increased generalizability. This dissertation delves into three core research questions, each …

The Aging Workforce: How It Relates To Incident Rates Within A Distribution Warehouse And A Chemical Manufacturing Building, Elisabeth V. Jones

USF Tampa Graduate Theses and Dissertations

Over the past 20 years or so, the average age of the global population has slowly increased. This is due to low birth rates as well as increased life expectancy. With this global population aging, there has also been a shift in the global labor market causing a trend termed the “aging workforce.” However, with the aging workforce comes a new set of issues for the health and safety professional because they create new hazards. This is due mostly because of the body undergoing physical, psychological, and cognitive decline as someone ages which causes decreased capabilities. Thus, the purpose of …

Cyber-Physical Multi-Robot Systems In A Smart Factory: A Networked Ai Agents Approach, Zixiang Nie

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on addressing the technical challenges of non-stationarity in smart factories through the use of cyber-physical AI agents. Industry 4.0 and smart manufacturing with smart factories as a central role, have a growing demand for Just-in-Time (JIT) and on-demand production, as well as mass customization—all while maintaining high productivity, resource efficiency and resilience. This research positions Multi-Robot Systems (MRS)-driven smart factories. The heterogeneous production and transportation robots in an MRS collaborate to form multiple real-time adjusted production flows achieving the flexibility to accommodate such on-demand, mass customization.

However, the implementation of MRS introduces new sets of challenges, including …

Process Automation And Robotics Engineering For Industrial Processing Systems, Drake Stimpson

USF Tampa Graduate Theses and Dissertations

Automation in industrial systems applications has emerged as the fundamental solution for improving quality, production rate, and efficiency of a process. Much of the recent popularity surrounding the transition of processes from manually operated tasks to automated systems can be attributed to the concept of Industry 4.0, which outlines the fundamental guidelines for integrating cyber-physical systems into industrial processes. Due to rapid advancement of technology in robotics and automation as well as the increase in accessibility of resources to this technology, the capability to develop automated systems has become feasible for small-scale enterprise. This work presents a two-part initiative to …

Stochastic Models For Resilience Assessment And Improvement, Weimar Ardila Rueda

USF Tampa Graduate Theses and Dissertations

Resilience refers to the ability of a system to absorb and mitigate the impact of potential disruptions and return to normal operational conditions. The above notion of resilience allows us to distinguish two stages in a system's post-disruption response, the absorptive dimension and the recovery dimension. The absorptive dimension is related to a system's robustness and capacity to mitigate initial loss posterior to a disruption. Meanwhile, the recovery dimension is the system's rapidity to return and reach an acceptable level of functionality after the disruption occurrence.

A social-physical (SP) system's post-disruption response is related to its resilience capacity so building …

Degradation Performance Modeling And Optimal Maintenance Planning Of Deteriorating Critical Infrastructures, Hung Q. Nguyen

USF Tampa Graduate Theses and Dissertations

Serving as the backbone of the nation’s economy, a large and growing number of deteriorating critical infrastructures, such as transportation and water infrastructures, are underperforming, aging, becoming structurally deficient, and must be repaired or replaced. Due to the influence of a variety of factors (e.g., material structure, design, operation, and environmental conditions) at different phases of lifecycle and the costly data acquisition process, field degradation of deteriorating infrastructures is highly uncertain with limited degradation data. The co-location and spatial proximity between many infrastructures, such as road and water infrastructures, further makes them physically and operationally interdependent. The sheer deterioration of …

Computational Methods For Solving The Combinatorial Optimization Problems In Transportation, Xufei Liu

USF Tampa Graduate Theses and Dissertations

This dissertation discusses three transportation problems. The first problem is a bi-level optimization problem that simultaneously optimizes facility locations and network design in hazardous materials transportation. In the upper level, the leader intends to reduce the facility setup cost and the hazmat exposure risk, by choosing facility locations and road segments to close for hazmat transportation. When making such decisions, the leader anticipates the response of the followers who want to minimize the transportation costs. A robust optimization approach with multiplicative uncertain parameters and polyhedral uncertainty sets is applied to deal with the uncertain risk and demand.

The second problem …

Statistical Monitoring The Quality Of Healthcare Services, Yanqing Kuang

USF Tampa Graduate Theses and Dissertations

In today’s healthcare industry, quality of care is a growing focus in the delivery of healthcare. To improve the quality of care in healthcare delivery, many studies focus on the longterm operational decision making to meet the expectations of healthcare providers and users, such as medical resource allocation, bed planning, staff scheduling, etc. These problems are typically parts of long-term operational decision making, however, time is essential in healthcare system. To ensure the adherence to a high quality of care and detect deterioration in real time, the quality of service should be measured over days or hours instead of just …

Theory And Algorithms For Systems Optimization, Vahid Mahmoodian

USF Tampa Graduate Theses and Dissertations

This dissertation presents four sets of contributions in the field of theory and algorithms for system optimization. In the first set, we introduce a simulation optimization method for redistributing bikes in a free-floating bike sharing system. The second set of contributions is a framework for batching queries in large databases to optimize the data retrieval time. The third set includes two branch-and-bound algorithms to solve minimum multiplicative programming problems and one branch-and-bound algorithm to solve the maximum form of the mentioned problems. At last, the fourth set presents an approach to fairlyassign delivery tasks in an outsourcing last-mile delivery system …

Healthcare Data Analytics For Predicting Health Outcomes Of Older Adults And Emergency Responses Of Aged Care Facilities, Nazmus Sakib

USF Tampa Graduate Theses and Dissertations

The United States (US) is experiencing rapid growth in its older adult population, who may suffer from multiple chronic diseases, injuries, and impairments. To meet with the excess demand without compromising the quality of care for older adults, the current aged care systems, such as nursing home systems, will face unprecedented challenges of healthcare resource shortage with rising costs. Accurate prediction of health outcomes of individual older adults will facilitate the aged care professionals to better prioritize healthcare resources for the most at-risk individuals with more focused care and provide more proactive and individualized treatment and care delivery. In addition, …

Analysis And Modeling Of Strategic Interactions In Health Systems To Improve Patient Care Access, Jorge A. Acuña Melo

USF Tampa Graduate Theses and Dissertations

Affordable health care access that provides well-coordinated and high-quality services on time is a goal that governments and health organizations strive for. Regrettably, most countries deal with access problems that affect the population's health, such as long waiting lists for specialized medical services, overcrowding of emergency departments, and high health prices. In the present doctoral dissertation, I model and analyze the strategic interactions that inhabit the health system machinery to uncover possible structural problems that led to the aforementioned issues. The study involves operation research, data science, and game theory techniques to address the health care access predicament.

Each research …

Maximum Multiplicative Programming: Theory, Algorithms, And Applications, Payman Ghasemi Saghand

USF Tampa Graduate Theses and Dissertations

This dissertation presents three different contributions to an important class of optimization problems known as Multiplicative Programs (MPs). The first group of contributions contains the development and analysis of several multi-objective optimization-based based algorithms designed to find the optimal solution of Mixed Integer Linear MPs. As for the second group, the application of a special class of MPs in radiotherapy planning is presented. Finally, in the last group, a new technique for conducting the multiplication process in the objectives of MPs is presented. Using this technique, we introduce a family of novel solution methods that are capable of solving both …

Data-Informed Decision Support To Improve Pediatric And Maternal Care Quality Under Medicaid Managed Care Settings, Hasan Symum

USF Tampa Graduate Theses and Dissertations

Over the last two decades, the United States has spent almost twice as much per person in healthcare compared to most other wealthy countries. However, this higher spending has not necessarily transformed into improved quality of care; According to World Health Organization reports, the US now ranks 39th for child health and wellbeing and worst in maternal care among developed nations. In terms of proportion of preventable hospital visits, low-risk cesarean sections, and avoidable maternal morbidity/death, the U.S. is among the highest compared with the peer nations. The prevalence of these adverse outcomes in pediatric and obstetric care is particularly …

Strategies For Achieving The United States Health System's Quadruple Aim By Enhancing The Primary Care Level, Jennifer L. Mendoza-Alonzo

USF Tampa Graduate Theses and Dissertations

The quadruple aim is an approach to optimize the performance of the health system in the United States and consists of four dimensions. The main objective is to improve the population's health, followed by reducing cost, improving patients' experience, and increasing providers' satisfaction. In the present doctoral dissertation, I explore three strategies that help accomplish the quadruple aim at the primary care level. The analysis combines data science and operation research principles to address health system engineering questions.

Each strategy proposed in this document emphasizes one objective more than another; however, all of them in conjunction serve to attain the …

Heterogeneous Performance Modeling With Applications In Healthcare And Reliability Engineering, Xuxue Sun

USF Tampa Graduate Theses and Dissertations

In both health systems engineering and reliability engineering, individual units, such as patients and product units, often exhibit highly heterogeneous performance due to the influences of various observed individual characteristics and unobserved/unknown factors. Successful modeling of the heterogeneous performance of individual units is of great importance. It will not only facilitate the identification and quantification of influencing factors for improving performance of individual units, but also improve prediction accuracy of their future performance. This will further facilitate better decisions, such as cost-effective and adaptive healthcare resource planning decision, and proactive maintenance policy at reduced cost. However, due to the highly …

The Utilization Of Shared Energy Storage In Energy Systems: Design, Modeling And Optimization, Rui Dai

USF Tampa Graduate Theses and Dissertations

Energy storage (ES) plays a significant role in modern smart grids and energy systems. With the advances of ES technologies, efficiently applying ES to energy systems has become the bottleneck for achieving the benefits of ES. The traditional approach of utilizing ES is the so-called distributed framework in which there is a separate ES for each individual user. Due to the inherent limits in the distributed framework such as cost inefficiency and space limitations, many studies have promoted to utilize a shared ES in energy systems to further exploit the potentials of ES. However, current studies always focus on maximizing …

Efficient Neural Architecture Search With Multiobjective Evolutionary Optimization, Maria Gabriela Baldeón Calisto

USF Tampa Graduate Theses and Dissertations

Deep neural networks have become very successful at solving many complex tasks such as image classification, image segmentation, and speech recognition. These models are composed of multiple layers that have the capacity to learn increasingly higher-level features, without prior handcrafted specifications. However, the success of a deep neural network relies on finding the proper configuration for the task in hand. Given the vast number of hyperparameters and the massive search space, manually designing or fine-tuning deep learning architectures requires extensive knowledge, time, and computational resources.

There is a growing interest in developing methods that automatically design a neural network´s architecture, …

Using Optimization Methods For Solving Problems In Sustainable Urban Mobility And Conservation Planning, Zulqarnain Haider

USF Tampa Graduate Theses and Dissertations

This dissertation considers three separate optimization problems related to sustainable urban and environmental systems. The first problem relates to the nightly relocation and recharging operations for Free-floating electric vehicle sharing (FFEVS) systems. Such operations involve a crew of drivers to move the shared electric vehicles (EVs), and a fleet of shuttles to transport those drivers. Mixed integer programs are used to model the relocation and recharging operations. Two approaches are devised: sequential and synchronized approaches. In the sequential approach, the movement of EVs is first decided, then the routing of shuttles and drivers is determined. In the synchronized approach, all …

Identification Of Patterns And Disruptions In Ambient Sensor Data From Private Homes, Yan Wang

USF Tampa Graduate Theses and Dissertations

The world’s population is rapidly aging and the increasing demand for home and health care services from this aging population brings unprecedented challenges to the economy and society. Ambient-assisted smart homes, residences equipped with ambient sensors to monitor the resident’s daily activities in a continuous and unobtrusive way, present great potential to manage the growing care service needs of this older population segment, and enable them to age-in-place.

Despite growing research, using ambient sensor data from private homes to monitor daily activities, health and wellness still faces significant challenges. To study ambient sensor data from private homes where annotated data …