Digital Commons Network^™

Quantifying Lock Criticality For Inland Waterway Navigation Using An Agent-Based Simulation, Ashwin Narayan

Industrial Engineering Undergraduate Honors Theses

Inland waterway travel has tremendous potential to improve multimodal transportation in the United States due to its environment-friendly and safe nature. Waterway travel has proven to be much cheaper per ton-mile and more fuel efficient than trucks. Millions of dollars have recently been invested in inland waterway networks to facilitate travel. The waterway infrastructure must be resilient to enable efficient waterway travel and prevent unnecessary delays. Locks, an important component of waterway infrastructure, enable vessels to travel between waterways of varying depths and must be maintained consistently to avoid unexpected failures. However, with limited resources, it is difficult to preserve …

Feasibility Assessment And Container Traffic Forecasting Of Inland Waterway Container On Barge Transportation, Fan Bu

Graduate Theses and Dissertations

Container on Barge (COB) transportation is an intermodal freight transport mode that moves shipping containers via barges on navigable inland and intracoastal waterways. During the past twenty years, COB has been a growing mode of container shipping globally due to its low-cost, eco-friendly, and congestion-reducing characteristics. Europe and China are currently leading global COB transportation, and the United States (U.S.) may have the potential to achieve economic benefits through the implementation of COB within its intermodal transportation system. To explore this potential, this dissertation investigates the implementation feasibility of COB transportation within the U.S. intermodal freight transportation system. Three contributions …

Leveraging Machine Learning And Stochastic Programming To Address Vaccine Hesitancy In Public Health Resource Allocation, Hieu Trung Bui

Graduate Theses and Dissertations

Infectious disease outbreaks highlight the urgent need for effective strategies to distribute vaccines and allocate critical healthcare resources to contain the disease and reduce its negative impacts on the population. Managing these allocations is a significant challenge, especially in marginalized communities facing uncertainty in healthcare demand and logistical constraints. This dissertation addresses these challenges by investigating factors that influence dynamic changes in vaccine hesitancy (VH) and its implications for disease spread and healthcare resource demand. It develops optimization models for vaccine distribution and resource allocation under uncertainty, validated with data from the COVID-19 pandemic in the U.S. The first study …

Exploring Telehealth Utilization Through Data Analytics, Statistical Analyses, And Machine Learning Techniques, Aysenur Betul Cengil

Graduate Theses and Dissertations

This dissertation investigates the utilization of telehealth services, initially focusing on the Arkansas healthcare system and then extending the analysis nationwide. It aims to understand the factors influencing telehealth adoption and its impact on healthcare delivery. After examining telehealth utilization in Arkansas from 2018 to 2022, the research utilizes a comprehensive dataset from Epic Cosmos, which includes a wide range of patient and visit data from multiple healthcare facilities across the United States from 2018 to 2023. This timeframe allows for a detailed analysis of telehealth trends before, during, and after the COVID-19 pandemic. In Chapter 2, we analyze key …

Model-Based Comparison Of Biological Organism And Electro-Mechanical System Resiliency Strategies, Nicholas Ratycz

Mechanical Engineering Undergraduate Honors Theses

Bio-inspired design has been used by many engineers to solve difficult problems or to make manufacturing processes more efficient. Biomimetics is the study of implementing the structure or function of biological substances, materials, mechanisms, and processes onto artificial ones that mimic the original. The goal of the BIASD tool is to provide bio-inspiration for engineers by studying the fault-adaptive strategies that biological systems use, rather than just their structure or function. In this thesis, the fault adaptive strategies of both a biological example and that of a real cubesat are compared using three types of model-based system diagrams to show …

Comparing North American Professional Sports League Season Formats Using Monte Carlo Simulation, Lathan Gregg

Industrial Engineering Undergraduate Honors Theses

Each NFL, NBA, and MLB season consists of a regular season, in which teams play a set number of scheduled games and a playoff, in which qualifying teams compete for a championship. At the conclusion of each season, teams are ranked based on their performance throughout the season. This study aims to investigate the ability of each league's season format to accurately rank teams using Monte Carlo simulation. Matches between two teams are simulated by using the team’s assigned strength ranks to calculate a winning probability for each team. The winning probabilities are simulated with different skill values, dictating how …

Sequential Optimization For Stressor-Informed Test Planning Through Integration Of Experimental And Simulated Data, Jacob Brecheisen

Data Science Undergraduate Honors Theses

This technical report details an innovative approach in reliability engineering aimed at maximizing system durability through a synergistic use of physical experimentation and computer-based modeling. Our methodology explores the efficient design and analysis of computer experiments and physical tests to facilitate accelerated reliability growth, while leveraging a sequential integration of data from these two distinct sources: costly physical experiments, characterized by random errors, and inexpensive computer simulations, marked by inherent systematic errors. The key innovation lies in the adoption of a closed-loop design and analysis method. This method begins by identifying a viable subset of important environmental stressors—such as temperature, …

Open-Source Optimization For Green Last Mile Delivery And Other Applications, John Sooter

Industrial Engineering Undergraduate Honors Theses

Solving combinatorial optimization problems at scale and of sufficiently interesting context has historically required commercial solvers and access to proprietary company data. The development of performant open-source mathematical programming software and crowdsourced datasets has created an opportunity for individuals and enterprises alike to consider alternative solutions to problems with social and personal implications. This honors thesis represents a summary of my undergraduate research work, an application of optimization to three distinct problems connected to these developments. First, we present an optimization study of a last mile delivery system that shows optimization for energy consumption can generate vehicleindependent fuel savings at …

Using Convolutional Neural Networks For Autonomous Drone Navigation, Joshua Jowers

Industrial Engineering Undergraduate Honors Theses

Unmanned Aerial Vehicles (UAVs), more commonly known as drones, serve various purposes, notably in military applications. Consequently, there arises a need for navigation methods impervious to intercepted signals [1]. Previous research has explored numerous solutions, including machine learning. This paper delves into a specific machine learning approach employing a Convolutional Neural Network (CNN) to discern image locations [2]. It elucidates the conversion of a CNN model between two machine learning libraries and presents results from multiple experiments examining parameters and factors influencing the approach's efficacy. These experiments encompass testing different data sources, image quantities, and processing pipelines to gauge their …

Reliability Modeling And Improvement Of Critical Infrastructures: Theory, Simulation, And Computational Methods, José Carlos Hernández Azucena

Graduate Theses and Dissertations

This dissertation presents a framework for developing data-driven tools to model and improve the performance of Interconnected Critical Infrastructures (ICIs) in multiple contexts. The importance of ICIs for daily human activities and the large volumes of data in continuous generation in modern industries grant relevance to research efforts in this direction. Chapter 2 focuses on the impact of disruptions in Multimodal Transportation Networks, which I explored from an application perspective. The outlined research directions propose exploring the combination of simulation for decision-making with data-driven optimization paradigms to create tools that may provide stakeholders with optimal policies for a wide array …

Characterizing Logistics Operations Within A Federal Staging Area For Hurricane Response: A Qualitative Analysis Of Federal, State And Local Perspectives, Jannatul Shefa

Graduate Theses and Dissertations

A successful deployment of logistics operations following a disaster is a collective contribution of federal, state, and local entities to ascertain an efficient and effective response. This research analyzes data from interviews with disaster response logistics experts from these entities. The objective is to investigate the information sources and planning processes used in these organizations to plan vehicle routes for critical resource deliveries to impacted areas. Special attention is directed to the impacts of incomplete knowledge of infrastructure status, such as road disruptions due to debris or flooding. Supported by both qualitative and quantitative evidence, the study finds that incomplete …

The Impact Of A Carbon Tax On Emissions, Jessica Creech

Industrial Engineering Undergraduate Honors Theses

A carbon tax is an economic policy that aims to reduce various emissions to serve the protection of the environment. Versions of this policy have been implemented in multiple countries across the world to introduce a cost for contributing to environmental damage. Since climate change is prevalent in today’s world, finding an effective method to reduce emissions is essential. However, many countries hesitate to utilize a carbon tax for two reasons. First, they are unsure if the carbon tax is effective at reducing emissions. Second, there is a concern that the implementation of such a tax will be detrimental to …

Testing The Effects Of Different Designs On The Physical Properties Of 3d-Printed Watch Bands, Ross Harper

Industrial Engineering Undergraduate Honors Theses

Technological innovation progresses at an ever-increasing rate, and this is especially true in the field of 3D-printing. 3D-printing has become popular in manufacturing settings and among amateur hobbyists alike, largely because 3D-printers can fabricate an enormous number of designs from an array of materials and allow for fine-tuning through several setting options. Individuals with proficient 3D-printing abilities can produce a nearly infinite number of components for diverse applications in manufacturing, recreation, ergonomics, and many more. Some individuals use their skills to create functional substitutes for name-brand items, including bands to fit and be worn with a smart watch. However, little …

Detecting Pathobiomes Using Machine Learning, Valerie Jackson, Valerie Jackson

Industrial Engineering Undergraduate Honors Theses

Machine learning is a field with high growth potential due to the overall continuous progressions, developments, advancements, and improvements caused by the way it is used to help interpret and use large amounts of data [1]. One type of data that can be collected and analyzed by these machine learning models is data that is associated with DNA and information that the DNA gives. The research will be focusing specifically on using machine learning technology to detect pathobiomes indicative of salmonella pork. The pathobiome associated with salmonella is very similar to others, and this causes a problem for classification/detection with …

Automated Visualization Pipeline For Near Real-Time Risk Management System, Paris Joslin

Industrial Engineering Undergraduate Honors Theses

In modern society, technological capabilities and the amount of data readily available to users continue to grow exponentially. Many have adopted these new capabilities but lack the infrastructure needed to efficiently utilize high-powered software and programs. Without a method to collect, store, and process large datasets in real-time, individuals and businesses can quickly become overwhelmed, inhibiting effective decision-making processes. There is potential to improve decision-making abilities by enhancing the computing infrastructure. To accomplish this task, we will explore the ideas surrounding High Performance Computing (HPC) and data visualization software. High Performance Computing is the ability to process data and perform …

Interaction Effects And Selecting Regression Models Of Taylor Swift Song Popularity, Halle Schneidewind

Industrial Engineering Undergraduate Honors Theses

Understanding music popularity and what drives it is important not only for artists but for other individuals who are financially tied to music sales including producers, writers, and record labels. Studies have been done to define how a song’s popularity can be measured, what attributes or features are drivers for popularity, and to what extent can a song’s popularity even be predicted. This paper takes two linear regression approaches to predicting the popularity of a Taylor Swift song on Spotify based on auditory features the Spotify API estimates and historic popularity of songs on Spotify. One model takes into consideration …

Demand Prediction And Inventory Management Of Surgical Supplies, Rajon Paul Pantha

Graduate Theses and Dissertations

Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for …

Automation Of Life Cycle Assessment, Jacob Hickman

Graduate Theses and Dissertations

An automation program, named Jacob LCA, was created to help perform life cycle assessment (LCA). The program uses a template file to perform controlled and consistently ordered actions with the LCA program, SimaPro, and effectively removes the need for manual user input. It can be set to run using data from one or more life cycle inventory (LCI) files, which allows for rapid iteration and testing of data. It also partially addresses some of the limitations of LCA by establishing a procedure through which temporal and spatial variations in data can be integrated into LCI files and then passed to …

Efficient Routing For Disaster Scenarios In Uncertain Networks: A Computational Study Of Adaptive Algorithms For The Stochastic Canadian Traveler Problem With Multiple Agents And Destinations, Neel Chanchad

Graduate Theses and Dissertations

The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as …

Simulating Emergency Evacuation Response In An Auditorium Space, Anna Lee

Industrial Engineering Undergraduate Honors Theses

The successful execution of emergency evacuations is very important for the protection of the public. Some emergency events, such as fires, can occur with very little warning and turn into a dangerous situation in less than a minute. With high population densities, universities have increased risk involved with evacuations. One specific area that presents high risk is auditorium spaces such as lecture halls with high densities combined with added barriers such as tables and chairs. The ability to assess a building’s emergency preparedness is necessary for keeping the public safe. Simulation is a way to conduct a theoretical event and …

Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells

Graduate Theses and Dissertations

Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal …

A Multi-Criteria Ranking System For Prioritizing Maintenance Of Levee Systems In Arkansas, Nguyen Danh Phan

Graduate Theses and Dissertations

There are 208,009 properties in Arkansas that have more than a 26% chance of being severely affected by flooding over the next 30 years, which represents 13% of all properties in the state. A levee system is designed to reduce the flooding risk for urban and rural communities; however, most of the state's levees have been significantly outdated or built with engineering standards less rigorous than current best practices. The Levee Safety Action Classification (LSAC), as recorded in the National Levee Database (NLD), communicates the risk associated with living behind a particular levee and assists local, state, and federal stakeholders …

Modeling The Impact And Accelerating The Process Of Transitioning To A Sustainable Healthy Diet Through Decision Support Systems, Prince Agyemang

Graduate Theses and Dissertations

Food production and consumption are essential in human existence, yet they are implicated in the high occurrences of preventable chronic diseases and environmental degradation. Although healthy food may not necessarily be sustainable and vice versa, there is an opportunity to make our food both healthy and sustainable. Attempts have been made to conceptualize how sustainable healthy food may be produced and consumed; however, available data suggest a rise in the prevalence of health-related and negative environmental consequences of our food supply. Thus, the transition from conceptual frameworks to implementing these concepts has not always been effective. This paper explores the …

Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson

Graduate Theses and Dissertations

This research proposes problems, models, and solutions for the scheduling of space robot on-orbit servicing. We present the Multi-Orbit Routing and Scheduling of Refuellable On-Orbit Servicing Space Robots problem which considers on-orbit servicing across multiple orbits with moving tasks and moving refuelling depots. We formulate a mixed integer linear program model to optimize the routing and scheduling of robot servicers to accomplish on-orbit servicing tasks. We develop and demonstrate flexible algorithms for the creation of the model parameters and associated data sets. Our first algorithm creates the network arcs using orbital mechanics. We have also created a novel way to …

Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad

Graduate Theses and Dissertations

In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …

Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey

Graduate Theses and Dissertations

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …

Implementing The Cms+ Sports Rankings Algorithm In A Javafx Environment, Luke Welch

Industrial Engineering Undergraduate Honors Theses

Every year, sports teams and athletes get cut from championship opportunities because of their rank. While this reality is easier to swallow if a team or athlete is distant from the cut, it is much harder when they are right on the edge. Many times, it leaves fans and athletes wondering, “Why wasn’t I ranked higher? What factors when into the ranking? Are the rankings based on opinion alone?” These are fair questions that deserve an answer. Many times, sports rankings are derived from opinion polls. Other times, they are derived from a combination of opinion polls and measured performance. …

Comparing Actively Managed Mutual Fund Categories To Index Funds Using Linear Regression Forecasting And Portfolio Optimization, Luke Weiner

Industrial Engineering Undergraduate Honors Theses

The global investment industry offers a wide variety of investment products especially for individual investors. One such product, index funds, which are younger than actively managed mutual funds, have typically outperformed managed funds. Despite this phenomenon, investors have displayed a tendency to continue investing in actively managed funds. Although only a small percentage of actively managed funds outperform index funds, the costs of actively managed funds are significantly higher. Also, managed fund performances are most often determined by their fund category such as growth or real estate. I wanted to answer the following question for individual investors: can we …

A Spatiotemporal Analysis Of Food Pantry Accessibility In Washington County, Arkansas, Coleman Warren

Industrial Engineering Undergraduate Honors Theses

Food pantries are an essential resource for impoverished and food insecure communities. Washington County, Arkansas has a food insecurity rate of 14.3% as compared to the national average of 10.9% (Feeding America, 2019). The Northwest Arkansas Food Bank has a robust pantry network in Washington County to support families and individuals who struggle with food insecurity.

We conducted a spatiotemporal analysis of food pantry accessibility in Washington County, Arkansas to evaluate the effectiveness of the food pantry network in Washington County at supporting communities with the most need. This analysis was conducted using the Two-Step Floating Catchment Area (2SFCA) method …

Academic Advising Support Tool: An Optimization Approach, Spencer Loper

Industrial Engineering Undergraduate Honors Theses

More than ever, a college education is necessary to remain competitive in the job market. Therefore, colleges are dedicating numerous resources to ensure student success. Nonetheless, one of the most important factors of student success is proper academic advising. Students at the University of Arkansas and more specifically within the department of Industrial Engineering department are fortunate to have access to fantastic advising. However, given the volume of students, academic advisors do not have the time to talk through the nuance of every student’s long-term academic plan. The department does provide an eight-semester plan; however, students who have deviated from …