Operations Research, Systems Engineering and Industrial Engineering Commons^™

Uncertainty Quantification, Propagation & Conjunction Assessment In Orbital Mechanics Using Generalized Polynomial Chaos Expansion & 2-Dimensional Conjunction Plane Analysis Techniques, Monalisa Karim

Mechanical and Aerospace Engineering Theses

Uncertainties, that are inherent to dynamic models, can be associated with state initial conditions, force modelling errors, navigation and actuation errors. In system modelling stochastic differential equations are used to represent dynamic phenomena with uncertainties, for which the solutions are probability density functions of quantities of interest characterizing the realization of the stochastic processes. In Polynomial Chaos Expansion (PCE) propagation, these solutions are represented as weighted sums of multivariate spectral polynomials that are functions of the input random variables. Generalized polynomial chaos expansion (gPC) is an extension to the original homogenous PCE which projects the random solution onto a basis …

Anaerobic Digestion Of Food Waste Components: Modeling Biogas Production, Opeyemi E. Adelegan

Civil Engineering Dissertations

Food waste constitutes the single largest component of municipal solid waste landfilled in the United States — approximately 22% of 292.4 million tons generated in 2018 — and its anaerobic decomposition releases methane, a greenhouse gas with global warming potential approximately 27–30 times that of carbon dioxide over a 100-year horizon (IPCC, 2021). Anaerobic digestion (AD) offers an alternative management pathway that recovers energy and produces nutrient-rich digestate, but AD performance varies by as much as five-fold across food waste streams (130–630 m³ CH₄ per Mg VS added), and existing predictive tools either treat food waste as a …

A Data Driven Approach To Student Success: Visualizing Engagement And Performance Metrics, Araohat Kokate

2025 Spring Honors Capstone Projects - Archive

Many tutoring centers lack tools to analyze and visualize key performance metrics, limiting data driven decision making. This study develops a data visualization feature for the CSE Student Success Center App at the University of Texas at Arlington, enabling administrators to track student engagement, tutor performance and session trends. Using the data of students and tutors, the feature provides interactive dashboards for real-time insights. Administrators can monitor attendance patterns, tutor workloads and booking trends, optimizing resource allocation. Findings indicate that real-time data visualization enhances decision-making, reducing manual effort while improving operational efficiency. This can further help improve student support services. …

Crime Theory Informed Agent-Based Modeling For Crime Prediction And Patrolling Route Optimization, Shohreh Moradi

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Crime reduction remains a global priority, demanding both accurate modeling of criminal dynamics and efficient allocation of scarce policing resources. To address these needs, this study presents a two‐fold framework that (1) simulates street‐level crime patterns using an agent‐based model (ABM) grounded in Routine Activity Theory (RAT), Rational Choice Theory (RCT), and Crime Pattern Theory (CPT), and (2) optimizes patrol routing through a time-dependent, multi‐visit mixed‐integer linear programming (MILP) formulation.

In the first component, we integrate real‐world crime, environmental, and census data to reproduce realistic offender, citizen, and Police behaviors, capturing where and when robbery, burglary, and larceny occur across …

Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring, Marina Materikina

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Despite the numerous research studies and interest in the non-intrusive load monitoring (NILM) area to improve energy efficiency, the problem of accurate and precise disaggregation of electrical devices has not been solved yet. The goal of our research is to build a method with a focus on higher accuracy on complex state-based appliances, which most approaches struggle to detect due to their power signal complexity and low consumption. Our approach is NILM with data-driven signatures (DS), with the ability to potentially predict power usage over time that would work great for suitable applications such as demand response, anomaly detection, and …

A Design And Analysis Of Computer Experiments Approach To Water Distribution Network Seismic Rehabilitation Optimization, Uthman Abiola Kareem

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Water is an essential part of human life. However, there are critical infrastructures that enable water availability in communities and homes. One of such is a water distribution network. Water distribution network performance depends on its reliability, which could be threatened by external agents like earthquakes. When earthquakes occur, they cause damages on some pipes within the distribution network and this limits performance of water distribution network. While earthquakes cannot be prevented, effective maintenance intervention may reduce the impact of earthquakes on water distribution networks. In order to develop an effective maintenance plan, researchers approach it in different ways. However, …

Analysis Of Crowd Logistics Networks Using Agent-Based Models, Preetam Kulkarni

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Crowd logistics is a system in which an online platform connects a group of non-professional couriers (crowd/carriers), who use their under-utilized resources to offer delivery service to other individuals or businesses (senders) for a fee. While crowd logistics platforms have the potential to offer more flexible and responsive delivery services for much lower rates than traditional logistics providers, it is difficult for platforms to be successful as it is challenging to meet carriers’ and senders’ expectations. Crowd logistics has been applied in the context of food and grocery delivery, parcel pickup and drop-off services and last-mile delivery, however, it has …

Representation Learning Of Point Cloud Data For Process Mining And Anomaly Detection In Complex Systems, Yujing Yang

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Complex systems, e.g., advanced manufacturing systems, are largely associated with dynamic and transient behaviors, resulting in condition changes and anomalies. Sensor-based condition monitoring is critical in detecting anomalies and supporting process monitoring and performance improvement for complex manufacturing systems. Traditional sensor-based monitoring approaches primarily focus on one-dimensional (1D) signals and two-dimensional (2D) images, which are limited in their ability to capture high-resolution spatial patterns pertaining to anomalies induced by systems’ condition changes, especially subtle ones. Recent advancements in three-dimensional (3D) sensing present a unique opportunity to address this limitation by enabling the capture of 3D point cloud data with micro-level …

Establishing Employee Sense Of Belonging In A Busy Work Environment, Zoe A. Rodriguez

2024 Spring Honors Capstone Projects - Archive

This honors thesis capstone project explores the concept of an employee’s sense of belonging within a busy work environment. A department within ABC Company faces operational challenges regarding their daily tasks. The Honors contribution addresses the comparison between the busy work environment at ABC Company and existing literature to provide scholarly-supported recommendations and mitigation strategies for ABC Company. Existing literature underscores the importance of understanding contributing influences on an employee’s sense of belonging within the workplace. The honors contribution adds value by closing the information gap within companies that operate under a busy work environment and strategies of how to …

3d Point Cloud Sensing And Analytics With Applications In Process Mining And Quality Control Of Additive Manufacturing, Zehao Ye

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

The rapid advancement in Additive Manufacturing (AM) technologies has developed significant innovations in various sectors, including medical, aerospace, and automotive industries. Despite these benefits, the adoption of AM is often hindered by quality inconsistencies related to the surface defects and geometrical inaccuracies in the fabricated products. These defects can significantly undermine the mechanical properties of the products, leading to material waste and potential safety issues. This dissertation addresses the critical challenges in quality control of AM processes through the integration of 3D point cloud data and machine learning techniques, aiming to enhance the reliability and efficiency of AM systems. The …

A Human Factors Approach To Improve Layout Design For A Virtual Reality-Based Training Platform, Md Humaun Kobir, Taufiq Rahman, Yiran Yang, Shuchisnigdha Deb

SAGE Open Access Agreement Publications-Archive

In manufacturing industries, equipment arrangement, and layout design are critical factors that directly influence productivity, workplace safety, and workers’ performance. Link analysis, as a human factors approach, has been widely used in industries for many years to improve layout design and machinery arrangement. This approach considers humans' physical and cognitive capabilities and movement limitations to find an optimal design. Virtual reality significantly impacts our society from product design to worker training. Hence, effective virtual training platforms require the same attention to layout design as manufacturing work settings which offer efficient testing of multiple layouts. This research focuses on developing a …

Probabilistic Multivariate Time Series Forecasting And Robust Uncertainty Quantification With Applications In Electricity Price Prediction, Jie Han

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Electricity price forecasting (EPF) is a crucial task for market participants seeking informed decisions in day-ahead electricity markets. The increasing penetration of stochastic renewable energy and the deregulation of electricity markets pose challenges to electricity price forecasting. Given the dependence of electricity prices on stochastic factors such as weather conditions, market dynamics, and customer behaviors, deterministic forecasting methods offer limited insight into the potential future states of energy prices in highly stochastic markets. In this study, a transformer-based electricity price forecasting (TDEPF) model was developed, utilizing a two-step training process and demonstrating superior performance compared to typical RNN models. Subsequently, …

Enhancing Experiential Learning Through Virtual Reality: A Case Study In System Design And Hazard Analysis, Rafia Rahman Rafa

Industrial, Manufacturing, and Systems Theses - Archive

ABSTRACT: The recent advancement of additive manufacturing (AM) technologies leads to an extensive need for an industrial workforce. Training in AM requires expensive capital investment to install and maintain this technology and proper knowledge about potential safety hazards. Experiential, immersive training platforms like Virtual Reality (VR) can overcome this challenge by providing opportunities for effective learning in a safe and controlled environment. VR can teach students through active participation and immersive, hands-on experiences, which is especially important for manufacturing processes involving high-risk conditions. VR can expose students to manufacturing hazards and allow them to learn through trial and error without …

Topics In Optimization For Sustainable Energy Planning, Bahareh Nasirian

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

ABSTRACT: Due to the current trend of rising energy demand, finding alternate energy sources is vital. Many cities consider renewable energy as a component of a sustainable future. Typically, organic wastes are considered renewable energy sources. Wastes can be converted to proper energy forms using waste-to-energy technologies. On the other hand, more renewable energy sources in the power system may increase energy market stochasticity, alter system operation, and pose new problems for the current supply and demand equilibrium, which consequently requires new control methods. Hence, taking into account the issues mentioned above, in this research, we address organic waste conversion …

Increasing The Safety Of Bicyclists Using A Cyclist Behavior Questionnaire And A Smartphone Based Application, Anika Jannat Rimu

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

ABSTRACT: Bicycling is beneficial for health, the environment, road users’ flexibility, and personal expenses. Compared to motor vehicles, they are an active mode of transport, cause minimum pollution, are affordable, and can easily navigate through the increasing traffic all over the world. This increase in traffic, however, also increases the possibility of crashes with motor vehicles. Bicyclists, being more exposed to traffic than drivers, suffer fatal consequences from a crash. Therefore, a standard tool is required to understand bicyclist behavior on the road. This tool can provide insights into bicyclists’ behavior so that appropriate infrastructure or policy changes can be …

Towards Sustainable Additive Manufacturing: Assessment Of Cost, Greenhouse Gas Emission, And Recyclability, Lei Di

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Additive Manufacturing technologies fabricate 3D objects layer by layer following a predesigned CAD model. Owing to the unique layer-wise production method, additive manufacturing offers competitive advantages in comparison with traditional subtractive manufacturing, such as shortened production time, increased design freedom, improved manufacturing capability and complexity, and reduced manufacturing waste. Numerous research studies have been conducted to design, understand, and improve additive manufacturing technologies in order to facilitate the implementation in the supply chain. On the other hand, with the rapid growth of additive manufacturing, sustainability issues that exist on both process level and supply chain level have started to receive …

Applications Of Probability Of Success In The Well Delivery Process To Improve Risk, Opportunity, And Cost Assurance, Romar Alexandra Gonzalez Luis

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Oil and Gas (O&G) well drilling is risky and expensive. The cost of drilling is typically underestimated, and there is little understanding of the certainty or Probability of Success (POS) of achieving the well objectives within the estimated cost. This dissertation presents, for the first time, a publicly available POS Cost method/tool that enables O&G operators to estimate the cost of well drilling substantially more accurately and improve the POS of accomplishing the drilling for the estimated cost. This POS Cost method employs a comprehensive, expert-based assessment of risks and risk mitigations that are incorporated into a Monte-Carlo-based simulation of …

Design And Development Of A Virtual Reality Training Platform For Fiber-Reinforced Composite Manufacturing, Taufiq Rahman

Industrial, Manufacturing, and Systems Theses - Archive

This study presents the design and development of a virtual reality (VR) training platform for manufacturing fiber-reinforced composites, a sophisticated and high-demand material in various industries. Due to the high costs and safety concerns associated with compression molding machines - essential equipment for this manufacturing process, the VR platform offers a promising alternative for training to educational institutions and manufacturing industries. The platform provides an immersive, interactive training and cost-efficient learning environment, allowing users to gain practical experience without the risks and expenses of physical training. The VR training module was developed using the Unity game engine and deployed on …

Machine Learning For Ultraviolet Spectral Prediction, Linh Ho Manh

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Machine Learning has found wide applications in material science, including dielectric polymers, superconducting materials, and drug property prediction. The use of data analytics and machine learning methods to predict Vacuum Ultraviolet (VUV) spectra by encoding molecular structure is gaining interest because high-quality VUV spectral prediction capability would enable the study of new molecules without costly wet-lab measurements. This dissertation aims to study feature representations for molecular structures that enhance the prediction of VUV spectra via machine learning models. Both interpretable machine learning and deep learning are studied. Chapter 1 provides an overview of VUV/UV spectra retrieval, and Chapter 2 reviews …

Additive Manufacturing Of Stretchable Piezo-Resistive Sensors: Fabrication And Performance Evaluation, Mohammad Ahnaf Shahriar

Industrial, Manufacturing, and Systems Theses - Archive

Additive manufacturing provides a distinctive layer-wise production method, which is efficient and effective, especially when fabricating products with complex designs and/or multiple materials. One of the promising applications of additive manufacturing is 3D printing flexible piezo-resistive sensors, which measure the strain of human motions by characterizing the changes in resistance. In this research, fused deposition modeling is used to fabricate stretchable piezo-resistive sensors that use thermoplastic polyurethane (TPU) as the stretchable layer and the mixture of TPU and carbon nanotube (CNT) as the electrically conductive layer. Extensive experimental efforts are dedicated to investigating proper mixing technique, filament preparation method, and …

Forming Coalitions And Sharing Payoffs In N-Person Normal Form Games, Emma Owusu Dwobeng

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

For a given n-person normal form game, we form all possible sets of mutually exclusive and collectively exhaustive coalitions of the n players. For each set of coalitions, we define a coalitional semi-cooperative game as one in which these coalitions are taken as the players of this new game, each coalition tries to maximize the sum of its individual players’ payoffs, and the players within a coalition cooperate to do so. For any coalitional semi-cooperative game, the goal of the original n players is to improve their individual payoffs obtained in a Greedy Scalar Equilibrium (GSE) of the original game, …

Lasso Based State Transition Modeling With Interactions In Adaptive Interdisciplinary Pain Management, Amith Viswanatha

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

The Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center has an interdisciplinary pain management program for chronic pain. This program treats patients with a holistic view of reducing chronic pain and improving their physical, mental, and social well-being through treatment interventions. The development of an adaptive treatment decision tool is main goal of the research project. This program is modeled as a two-stage adaptive treatment decision problem, with state transition models representing the transition of patient state, treatment, and outcome variables from stage 1 to stage 2. Interactions between the patient state and treatments …

A Hybrid Systems Model For Emergency Department Boarding Management, Eniola Oluwasola Suley

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

The purpose of this research is to examine methods for minimizing the influence of boarding on emergency department (ED) crowding outcomes. To accomplish this purpose, this research uses a hybrid systems model framework by combining agent-based simulation, predictive and optimization models to improve ED outcomes such as length-of-stay and left-without-being-seen rates. For the research, different types of simulation models were examined (discrete event and agent-based/discrete event combination) to identify the most parsimonious for studying ED boarding. Predictive models using simulation output were developed to understand the factors that influence future boarding levels as well as generate predictions. Research has previously …

Optimizing The Performance Of Analytical Chemistry Instrumentation, Srividya Sekar

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Surrogate Optimization and Global Optimization approaches to optimize underlying functions have been studied and used extensively in the field of Operations Research. However, there are very few instances where these approaches have been applied and tested in applications with uncertainty. Additionally, extensive focus and effort have been put into developing highly complex metamodels rather than globally optimizing these metamodels. In this study, we propose a Mixed Integer Quadratically Constrained Program (MIQCP) based approach that globally optimizes a Quintic Multivariate Adaptive Regression Splines (QMARS) metamodel. The QMARS-MIQCP based optimization is applied to a global optimization framework called QMARS-MIQCP-OPT to optimize several …

Exploring Deep Learning In Finance, Abhijit Anand Anand Deshpande

Industrial, Manufacturing, and Systems Theses - Archive

Financial market analysis is process of analyzing market closely and predict the next move of market whether it will go up or down using historical data. Financial market is stochastic and has rapid changes over time, therefore it is very difficult to predict. The main goal of this work is to understand novel approaches of machine learning in finance, data parsing techniques, labelling the financial data. Furthermore, understand state of art Transformer model and implement and compare results with other traditional machine learning algorithms. Experiment carried out in python along with pytorch.

Evaluating And Addressing The Transportation Challenges Of Small-Scale Farmers And Ranchers In Regional Food Systems, Narjes Sadeghiamirshahidi

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

During the last 20 years, there has been a significant increase in US consumers’ interest in local food. At the same time, there is substantial potential demand for crops produced by local small and mid-size farmers and ranchers whose market channels are short distance distributions Although there is a variety of definitions for local food, it often refers to direct-to-customer market channels for farmers and ranchers, including farmers’ markets and Community Supported Agriculture (CSA), as well as distribution through local businesses like restaurants, grocery stores, as well as local institutions such as hospitals and schools. However, small and mid-size farmers …

Leveraging Ai And Supply Chain Technologies With Thermal Imaging And Telemedicine For Early Detection And Prevention Of Covid-19 And Respiratory Infections In Urm Communities, Gohar Azeem

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

The underserved population could be at risk during the times of crisis, unless there is strong involvement from government agencies such as local and state Health departments and federal Center for Disease Control (CDC). The COVID-19 pandemic was a crisis of different proportion, creating a different type of burden on government agencies. Vulnerable communities including the elderly populations and communities of color have been especially hard hit by this pandemic. This forced these agencies to change their strategies and supply chains to support all populations receiving therapeutics. The National Science Foundation (NSF Award # 2028612) funded this research to help …

Affordable Autonomous Vehicles For Deployment After Disastrous Events, Shannon Abolmaali

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

In disastrous events such as hurricanes and tornadoes, it has been observed that people get stranded and helpless without a feasible way to escape during those emergency situations. This became very evident during hurricanes, such as Katrina and Ida affecting millions of people seeking immediate rescue efforts. With the use of artificial intelligence and machine learning, we envision an autonomous vehicle, AV, which is able to find the most optimal and safest way to help those who are stranded to get them to a safe location. Electric vehicles, EV, and Autonomous Vehicles, AV, is becoming the future; minimizing the carbon …

Design For Older Adults – Functional Limitations And Human Factors Engineering, Megumi Sato Hice

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Background: Throughout the aging process, people experience changes in functional ability and such changes can happen in physical and mental functions. As a result, older adults develop limitations in their capabilities to perform daily activities. Although many studies have done to identify functional limitations for elderly, many older adults still face negative consequences in daily living conditions due to their functional declines. One of the possible causes may be a lack of feedback or participations of elderly users in the design process. Other reason could be a lack of consideration to identify the needs for a specific task associated with …

One Step At A Time: Improving The Fidelity Of Geospatial Agent-Based Models Using Empirical Data, Amy A. Marusak

Industrial, Manufacturing, and Systems Theses - Archive

Agent-based modeling is frequently used to produce geospatial models of transportation systems. However, reducing the computational requirements of these models can require a degree of abstraction that can compromise the fidelity of the modeled environment. The purpose of the agent-based model presented in this thesis is to explore the potential of a volunteer-based crowd-shipping system for rescuing surplus meals from restaurants and delivering them to homeless shelters in Arlington, Texas. Each iteration of the model’s development has sought to improve model realism by incorporating empirical data to strengthen underlying assumptions. This thesis describes the most recent iteration, in which a …