Operations Research, Systems Engineering and Industrial Engineering Commons^™

Examining Award Compliance To Inform Resource Allocation, Jacob Haarala

Data Science Undergraduate Honors Theses

This project focuses on JB Hunt Transport Inc's intermodal business unit (JBI) by focusing on the challenges associated with Published Pricing and Contractual Pricing. The primary issue revolves around the variance between the awarded freight volumes in Requests for Pricing (RFPs) and the actual volumes realized when the freight is shipped. This discrepancy poses challenges for effective sales planning, revenue goals, and optimal freight network management within JBI. Reporting tools, such as PowerBI, are currently used by JBI to provide insights into award compliance on a weekly basis. However, our goal with this project was to provide a deeper understanding …

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 …

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 …

Smart Cities-A Structured Literature Review, Jose Sanchez Gracias, Gregory S. Parnell, Eric Specking, Edward A. Pohl, Randy Buchanan

Industrial Engineering Faculty Publications and Presentations

Smart cities are rapidly evolving concept-transforming urban developments in the 21st century. Smart cities use advanced technologies and data analytics to improve the quality of life for their citizens, increase the efficiency of infrastructure and services, and promote sustainable economic growth. Smart cities integrate multiple domains, including transportation, energy, health, education, and governance, to create an interconnected and intelligent urban environment. Our research study methodology was a structured literature review using Web of Science and Google Scholar and ten smart city research questions. The research questions included smart city definitions, advantages, disadvantages, implementation challenges, funding, types of applications, quantitative techniques …

Health-Care And Supportive Services In General Population Disaster Shelters, Ashlea Bennett Milburn, Charleen C. Mcneill, Lauren Clay, Janice Springer, Mary Casey-Lockyer

Industrial Engineering Faculty Publications and Presentations

Objectives:

The Communication (C), Maintaining Health (M), Independence (I), Services, Support and Self-Determination (S), and Transportation (T) is a framework (C-MIST) for identifying functional needs in an emergency response. A C-MIST documentation tool provides shelter staff with a list of potential client needs and actions to address them. This retrospective review describes the needs and actions indicated on completed C-MIST documentation tools (ie, records) within domestic general population shelters following Hurricane Florence in 2018.

Methods:

A convenience sample of 1209 records completed by shelter disaster health services personnel was provided by the American Red Cross. The records correspond to client …

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 …

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 …

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 …

Lead Distribution Modeling For Supply Chains With A Large Number Of Items, Wesley Tate

Industrial Engineering Undergraduate Honors Theses

Adding randomness into a simulation model allows for a better understanding of the variation that can occur in a real-life setting. This paper documents the methodology used to recommend a set of distribution models to cover administrative and production lead times for the simulation program involving hundreds of thousands of items. The problem of distribution fitting for large datasets is addressed, with histograms, Q-Q, and P-P plots being used to verify models in addition to goodness-of-fit test statistics. Variable level reduction using frequency and distribution matching approaches are outlined followed by the use of random forest modeling to identify key …

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 …

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 …

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 …

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 …

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 …

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 …

Machine Learning For Early Detection Of Pediatric Sepsis, Glory Manson-Endeboh

Graduate Theses and Dissertations

Sepsis is a host response to infection in both adults and children. It contributes to approximately 1.7 million cases annually with nearly 270,000 annual deaths in the United States. In the United States, the financial burden of sepsis on survivors and their families including the hospitals is over $4.8 billion, at approximately $64,280 per hospitalization. The first goal of this thesis research is to develop efficient machine learning models to predict pediatric sepsis accurately for inpatients. The second objective is to develop machine learning methods to determine how early sepsis can be detected to mitigate mortality. We examine data collected …

Advancing Statistical Learning And Decision Modeling Using Irregularly-Sampled Multivariate Data For Managing Respiratory Diseases, Maryam Alimohammadi

Graduate Theses and Dissertations

Complex healthcare systems require efficient and effective data-driven decision making in various aspects. As patient data becomes more available, advanced statistical learning and machine learning techniques are applied to improve data-driven decision making. However, patient health data, including clinical trial data, medical records, and electronic health records, are associated with several challenges. Patient health data includes medical information of a patient that may includedemographics, information relating to their health or illness, medications and treatments, etc. They are a combination of static and time series variables, with many censoring and missingness in the data, and are irregularly sampled in most cases. …

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 …

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 …

Dietary Practices During Pregnancy In A Marshallese Community: A Mixed Methods Analysis, Britni Ayers, Cari A. Bogulski, Ashlea Bennett-Milburn, Anna Fisher, Morda Newton, Pearl A. Mcelfish

Industrial Engineering Faculty Publications and Presentations

Dietary practices during pregnancy play a pivotal role in the health of women and their children and set the foundation for long-term health. Marshallese women have disproportionally higher rates of maternal and infant health disparities, yet little is known about the dietary practices during their pregnancy. The purpose of this study was to identify dietary practices during pregnancy among Marshallese women. From March 2019 to March 2020, a purposive sample of 33 pregnant Marshallese participants participated in a mixed methods study. Two primary themes emerged: (1) traditional beliefs about a healthy diet during pregnancy; and (2) dietary change during pregnancy. …

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 …

Assessing The Influence Of Health Policy And Population Mobility On Covid-19 Spread In Arkansas, Tayden Barretto

Industrial Engineering Undergraduate Honors Theses

The outbreak of COVID-19 has created a major crisis across the world since its start in 2019, and its influence on every realm of society is undeniable. Globally, more than 500 million cases have been recorded since March 2020, with almost 6 million deaths. In the wake of this crisis, many governments and health organizations have taken steps and precautions to mitigate its spread. These steps involve public mandates of information, reducing frequency of personal contact, and use of masks to minimize the risk of transmission. Current access to mobility data released from Google detailing population movements has provided a …

Analyzing Vulnerabilities In The Northwest Arkansas Highway Network Using Mathematical Optimization, Brandon Jerome

Industrial Engineering Undergraduate Honors Theses

The highway and bridge network is a critical infrastructure that allows for the free transportation of citizens and enables truck-borne freight transportation. Disruption of this system could be caused by a terrorist attack, natural disaster, growth of population, required repairs and upgrades, or collapse caused by old age or malfunction. In the event of a disruption cities and regions can experience increased traffic and supply chain shortages, thus causing cascading effects throughout surrounding areas. With this motivation, we develop a network interdiction optimization model to identify a limited subset of roads that, if disrupted, causes the greatest increase in 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. …

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 …