Operations Research, Systems Engineering and Industrial Engineering Commons^™

Seeing Feedback Differently: Enhancing Learning Through Individualized Video Feedback, Makenzie Khristine Keepers

2026 Scholarly Teaching Conference: Concurrent Session Papers

Students often seek feedback that goes beyond rubric scores, especially for complex assignments like project reports where expectations are nuanced. Transitioning from lengthy written comments to personalized video responses has proven to be an effective alternative. These videos provide students with clear explanations of strengths and areas for growth, while walking them through their work in detail. Feedback from learners suggest that video feedback feels more comprehensive and accessible, helping them better understand mistakes and apply corrections. Importantly, producing video feedback requires comparable effort to traditional written comments, yet offers greater impact for formative assessments that shape performance on summative …

Simulation Optimization Of Intermodal Freight Transportation Under Disruptions, Israt Humayra

Graduate Theses, Dissertations, and Problem Reports (ETD)

Rapid growth in freight transportation in modern supply chains has led to increased operational costs, congestion, and severe environmental impacts, especially greenhouse gas emissions. Combining different modes, such as highway, railway, and waterway, intermodal transportation could thus offer considerable benefit to improve efficiency, sustainability, and resilience. However, most existing planning approaches rely on simplified assumptions, fixed schedules, and average cost estimates, making them less relevant to dealing with real-world uncertainties and disruptions. This study develops a simulation-optimization framework for intermodal freight transportation under disruption. We develop a mixed-integer programming model to represent an intermodal logistics planning framework on a multi-layered …

Leveraging Physiological Signal Activity And Self-Report Data To Assess Students’ Trust In “My Friendly Mind” App And Its Impact On Their Mental Health Knowledge: A Mixed-Method Phase 1 Clinical Trial Focusing On Depression And Attention Deficit Hyperactivity Disorder From Human Factors Standpoint., Yeganeh Shahsavar

Graduate Theses, Dissertations, and Problem Reports (ETD)

Mental health issues have become a significant global public health concern, especially among younger generations. The growing number of mental health challenges, combined with limited access to quality care, makes the problem even worse. Studies reveal that over 70% of individuals worldwide in need of mental health services do not receive appropriate care. Digital health technologies have the potential to enhance mental health services by making them more accessible and affordable. Despite the increasing popularity of mental health mobile applications (mHealth), there remains a lack of robust evidence of their effectiveness and the level of user trust, particularly in areas …

Synthetic Data–Driven Early Prediction Framework For Acute Kidney Injury In Patients Receiving Vancomycin And Ceftazidime/Avibactam, Maryam Ramazani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Background: The nephrotoxic risks of combining ceftazidime/avibactam (AVI) with vancomycin (VAN) remain underexplored, despite both agents independently being linked to acute kidney injury (AKI). This study assessed the risk of AKI associated with concurrent VAN and ceftazidime/avibactam (VAN-AVI) therapy and developed synthetic data models to enable early prediction of AKI.

Methods: We conducted a retrospective analysis using electronic health record data from hospitalized adults between 2015 and 2022. The incidence of AKI was compared among patients receiving VAN-AVI or VAN in combination with piperacillin/tazobactam (VAN-TPZ) versus VAN monotherapy. AKI was defined as a composite of de novo and recurrent AKI …

Framework For Development Environment Selection In Digital Twin Applications, Carlos Dodero Fernandez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Digital Twin (DT) technology, a cornerstone of Industry 4.0, facilitates real-time synchronization between virtual models and physical manufacturing systems, enhancing operational efficiency and decision-making. However, its widespread adoption is hindered by the absence of standardized methods for selecting Development Environments (DEs) for DTs, compounded by challenges in cost, interoperability, and connectivity with Industrial Internet of Things (IIoT) protocols. This thesis proposes a Systematic Selection Framework to address this gap, offering a structured methodology to evaluate DEs based-on visualization quality, scalability, interoperability, and cost-effectiveness for manufacturing applications. The framework categorizes and compares sixteen DEs into Game Engines, Robotics Engines, and Simulation …

A Strategic Infrastructure Improvement Framework For Intermodal Transportation Networks, Ayoub Abusalih

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this research, we propose a novel approach to design infrastructure networks for intermodal freight transportation systems, which incorporates railways, highways, and inland waterways (IWW). The objective of our study is to identify the optimal set of hubs to be built and operated over an extended time, based on the projected domestic cargo demand. Unlike traditional hub location models, our approach introduces hybrid hubs, where hybrid transportation modes are integrated to facilitate cargo handling. This innovative integration enables more efficient intermodal connections, leading to tangible reductions in operating costs, and carbon emissions. Specifically, we propose a mixed integer programming model …

Enhancing Pipeline Simulations Through Artificial Intelligence And Machine Learning: A Smart Proxy Modelling Approach, Afeez Shittu

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Enhancing pipeline simulations is essential for improving operational efficiencies and effectively managing risks in the oil and gas industry. Traditional pipeline simulators, relying heavily on mathematical modeling assumptions, often face limitations due to their high energy and computational demands. This thesis addresses these challenges by introducing an innovative approach that integrates artificial intelligence (AI) and machine learning (ML) through a smart proxy model, offering a more efficient, cost-effective, and flexible alternative to conventional full-physics models used in pipeline simulation software.

The primary aim of this research is to develop and implement a smart proxy model capable of accurately predicting …

Changes In Psychiatric Diagnosis Associated With Sars-Cov-2 Infection And Predicting The Development Of New Psychiatric Illness In Covid Patients By Using Machine Learning Approach: A Study Using The Us National Covid Cohort Collaborative (N3c), Asif Rahman

Graduate Theses, Dissertations, and Problem Reports (ETD)

The enduring impact of COVID-19 extends beyond acute illness, with potential long-term psychiatric consequences raising significant concern among healthcare professionals and researchers alike. Emerging evidence suggests a multifaceted relationship between COVID-19 and the development of different psychiatric illnesses like Schizophrenia Spectrum and Psychotic Disorders (SSPD), Depression, Bipolar disorder, Personality disorder, Trauma, and a range of other mental health conditions. Considering these emerging connections, our study endeavors to rigorously assess the associations between COVID-19 and various psychiatric illnesses while simultaneously employing machine learning techniques to predict the development of new psychiatric disorders in individuals affected by the virus. Leveraging the extensive …

A Multimodal Physical Fatigue Assessment Method Using A Biomarker And Accelerometer-Embedded Wearable Wristband, Md Hadisur Rahman

Graduate Theses, Dissertations, and Problem Reports (ETD)

Physically demanding tasks pose significant challenges to worker health, safety, and productivity across various industrial sectors in the United States. The construction industry is particularly affected due to the labor-intensive nature of its tasks and harsh environmental conditions. The industry suffers from unsatisfactory occupational health and safety records, with physical fatigue being a major contributor. Physical fatigue not only affects individual well-being and workplace safety but also influences productivity and the United States’ economy. Given the high incidence of injuries and accidents in the construction industry, assessing physical fatigue has become critical for improving worker safety and productivity. To address …

Development Of Dynamic Mass-Energy-Thermodynamics Constrained Hybrid Neural Network Models For Process Systems Applications, Angan Mukherjee

Graduate Theses, Dissertations, and Problem Reports (ETD)

First-principles models can provide very good predictions even for cases when there are no data at all, or data are limited in certain range of operating conditions, or for cases where data collection is infeasible. However, the development of accurate first-principles models for complex nonlinear dynamic systems can be time consuming, computationally expensive, and may be infeasible for certain systems due to lack of sufficient knowledge (information). It is also challenging to adapt first-principles models for time-varying systems. Furthermore, it can be difficult, if not impossible, to develop accurate models for some complex phenomena that are poorly understood. On the …

Enhancing Robotic Exploration Through Semantically-Guided Sampling Strategies, Christopher Alexander Arend Tatsch

Graduate Theses, Dissertations, and Problem Reports (ETD)

From space and deep-sea exploration to disaster response and environmental monitoring, autonomous robots are essential for advancing science, improving safety, and addressing critical challenges. This dissertation introduces a novel open-source strategy for autonomous robotic exploration: the Semantically-Guided Exploration (SGE) framework. Designed for ground vehicles, SGE integrates semantic understanding into the autonomous exploration process, improving decision-making in complex environments. Specifically, the proposed sampling-based approach uses the information from the semantic segmentation of RGB images and depth images to guide the robot's selection of exploration goals. This method enables the robot to steer away from potential dangers such as large rocks and …

Data-Driven Approaches For Achieving Carbon Neutrality: Predictive Models For Reducing Co2 Emissions And Enhancing Industrial Sustainability, Farzana Islam

Graduate Theses, Dissertations, and Problem Reports (ETD)

In response to the escalating challenges posed by climate change and industrial inefficiency, this thesis presents a comprehensive investigation aimed at advancing the predictive modeling of global CO2 emissions and enhancing operational efficiency in steel manufacturing through Electric Arc Furnace (EAF) temperature optimization. Leveraging a rich dataset sourced from the World Development Indicators database alongside a meticulously curated dataset specific to EAF operations, our study applies an innovative blend of econometric and machine learning techniques, including Pooled Ordinary Least Squares (Pooled OLS), Random Effects (RE), Fixed Effects (FE), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. The …

A Mixed Methods Approach To Evaluation And Modeling Of Ergonomic Stressors Due To Mass Decedent Handling, Vaishakhi Suresh

Graduate Theses, Dissertations, and Problem Reports (ETD)

Work-related injuries, illnesses, and musculoskeletal disorders (MSDs) significantly impact the U.S. healthcare industry, leading to lost working days, high medical costs, and increased turnover rates among healthcare workers (HCWs). Increased physical and mental health risks due to the handling of deceased during COVID-19 pandemic has exacerbated these issues. While the ergonomic risks of patient handling are well-studied, limited research exists on the ergonomic risks of manual handling of the deceased. Therefore, the primary objective of this study was to explore the challenges associated with decedent handling so that effective safety interventions to reduce the risk of injury and mental distress …

Prediction Of Anomalous Events With Data Augmentation And Hybrid Deep Learning Approach, Ahmed Shoyeb Raihan

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this study, we propose a novel anomaly detection framework designed specifically for Multivariate Time Series (MTS) data, addressing the prevalent challenges in analyzing such complex datasets. The detection of anomalies within MTS data is notably difficult due to the complex interplay of numerous variables, temporal dependencies, and the common issue of class imbalance, where one category significantly outnumbers another. Traditional deep learning (DL) approaches often fall short in simultaneously tackling these issues. Our framework is designed to address these challenges through a two-phased approach. Phase I employs Conditional Tabular Generative Adversarial Networks (CTGAN) to create strategic synthetic data, setting …

Milk Collection Problem: Integrating The Traveling Salesman And Set Covering Problem - A Case Study In West Virginia, Usa, Md Rabiul Hasan

Graduate Theses, Dissertations, and Problem Reports (ETD)

Route determination for perishable products is complex due to its unique characteristics, such as limited shelf-life regulatory requirements, or possibility of getting damaged. This research investigates a novel problem of collecting raw milk from a rural network of dairy farms. The research problem is grounded in a real scenario of milk collection in West Virginia, USA. The milk in this scenario is produced by small farms incapable of realizing transportation economies of density out in mostly rural areas throughout the state. Maximum coverage area and milk processing overhead costs are used to identify suitable locations for intermediate milk collection centers …

Laser Deposition Additive Manufacturing Of Multi-Material And Metal-Ceramic Composite Structures, Manikanta Grandhi

Graduate Theses, Dissertations, and Problem Reports (ETD)

The increasing performance requirements of modern industrial systems, coupled with the imperative for decarbonization, necessitate a fundamental rethinking of metallic component design and manufacturing. Traditional materials, with their inherent limitations in property optimization, susceptibility to degradation, and weight constraints, are proving insufficient. Multi-material joining offers a potential solution, enabling designers to strategically integrate diverse materials with specific, tailored properties into single component. Functionally graded materials (FGMs) and oxide dispersion strengthened (ODS) materials are prime examples of this approach, delivering substantial improvements in wear resistance, thermal regulation, high-temperature resilience, and overall weight efficiency. However, conventional manufacturing approaches further exacerbate the limitations …

A Comparative Analysis Of Coastdown Testing Methods From An Electric Drive Unit Engagement Perspective Using A Student-Designed Parallel Hybrid Electric Vehicle, Dawson Everett Dunnuck

Graduate Theses, Dissertations, and Problem Reports (ETD)

Coastdown testing and road load determination are pivotal parts of the automotive design process. Vehicle manufacturers and independent companies perform and analyze road loads determined through a coastdown or similar method to determine a vehicle’s road load for modeling and EPA certification. For a traditional coastdown, the vehicle’s drivetrain must be disconnected through a clutch between the engine and the transmission while traveling at a high rate of speed to place the vehicle in neutral. This changes for hybrid and electric vehicles. Some hybrid, and most electric, vehicles delivered to customers do not have this clutch action to grant the …

Techno-Economic Analysis And Optimization Of Hydrogen And Mechanical Energy Storage Systems, Pavitra Senthamilselvan Sengalani

Graduate Theses, Dissertations, and Problem Reports (ETD)

The increasing significance of renewable energy sources is thrusting the load cycling of fossil-fueled power plants (FFPP), designed to operate under nominal-load conditions. Integration of energy storage systems (ESS) with the FFPPs such as hydrogen energy storage (HES) and mechanical energy storage facility such as compressed air energy storage (CAES) shows the potential to minimize the levelized cost of electricity during high demand scenarios and also minimize the negative impacts of off-design FFPP operation. The deployment of energy storage facilities at the FFPP level have considerable potential advantages as they can be exploited within the existing equipment items and facilities …

Energy Digital Twins In Smart Manufacturing Systems, Anna Billey

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this thesis, an Energy Digital Twin for smart manufacturing systems was developed and evaluated. In particular, the study focused on bidirectional parameter communication between the physical and the virtual part with the aim of optimizing the energy used in the manufacturing process. Rising costs and the environmental impacts related to energy consumption have grown in importance worldwide. There are elevated concerns in sectors like manufacturing, leading to an urgent quest to reduce energy consumption. A recent advancement in Industry 4.0 technology, the Digital Twin, represents a promising smart technology and tool that researchers are investigating to help reduce energy …

A Machine Learning Approach For Early Diagnosis Of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients, Tanjim Ahmed

Graduate Theses, Dissertations, and Problem Reports (ETD)

Transthyretin Amyloid Cardiomyopathy (ATTR-CM) is a rare, progressive, and fatal disease. Prevalence of ATTR-CM ranges from 4 to 17 per 100000 cases where the mean survival time is less than 4 years. It has a history of being underdiagnosed and misdiagnosed. The diagnosis delay has a weighted mean of 6.1 years for wild-type ATTR-CM. Low awareness, the necessity of invasive procedures, and lack of treatment are the key reasons for delayed diagnosis. But, with the introduction of non-invasive tests like nuclear scintigraphy with ^99mTC-PYP and the disease modifying drug Tafamidis, the diagnosis delay signifies a missed opportunity to increase …

Accelerating Manufacturing Decisions Using Bayesian Optimization: An Optimization And Prediction Perspective, Taofeeq Olajire

Graduate Theses, Dissertations, and Problem Reports (ETD)

Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, …

Simulating Energy Performance Of Buildings: A Study Using Equest And Energy Star® Portfolio Manager, Sabin Wagle

Graduate Theses, Dissertations, and Problem Reports (ETD)

The urgent need for improving building energy efficiency in response to global warming and environmental sustainability has highlighted the importance of practical techniques to optimize energy performance. The study utilizes the eQUEST simulation engine and Energy Star® Portfolio Manager to evaluate retrofit and design parameters and conduct a sensitivity analysis to explore the impact of different parameters on building energy performance. The study develops building energy models in eQUEST using data from two fully operational Distribution centers. It is calibrated using the Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method, meeting the …

Improving The Health And Safety Of Manufacturing Workers By Detecting And Addressing Personal Protective Equipment (Ppe) Violations In Real-Time With The Use Of Automated Ppe Detection Technology, Joseph Olufemi Fasinu

Graduate Theses, Dissertations, and Problem Reports (ETD)

The Centers for Disease Control and Prevention (CDC) emphasized that Personal Protective Equipment (PPE) can significantly reduce the risk of occupational injuries and illnesses. However, improper use, failure to use, and other PPE-related violations can still result in injuries and fatalities. Eye and face protection violation has been one of the top 10 most frequently violated OSHA standards in fiscal years 2018, 2019, 2020, 2021 and 2022 consecutively. A common practice among safety professionals to ensure PPE compliance has been to physically inspect or monitor PPE usage among workers, which has been found to be unsustainable on a continuous real-time …

Physics Infused Lstm Network For Track Association Based On Marine Vessel Automatic Identification System Data, Tasmiah Haque

Graduate Theses, Dissertations, and Problem Reports (ETD)

In marine surveillance, a crucial task is distinguishing between normal and abnormal vessel movements to timely identify potential threats. Subsequently, the vessels need to be monitored and tracked until necessary action can be taken. To achieve this, a track association problem is formulated where multiple vessels' unlabeled geographic and motion parameters are associated with their true labels. These parameters are typically obtained from the Automatic Identification System (AIS) database, which enables real-time tracking of marine vessels equipped with AIS. The parameters are time-stamped and collected over a long period, and therefore, modeling the inherent temporal patterns in the data is …

Analysis Of The Psychological And Production Effects Of The Use Of Gamification For Manufacturing Assembly, Makenzie Dolly

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this dissertation, the applications of gamification for manufacturing with a focus on effects to workers and productivity were studied. Gamification is a relatively new research area, with the term being officially defined in 2010. Since then, several fields (education, health, and marketing) have benefitted from its application. Despite exhibiting strong potential, the application of gamification had remained rather unexplored in the manufacturing domain. To explore this further, by employing a comprehensive literature review, four research gaps were identified: the need for i) the use and acceptance of Deterding’s definition of gamification, ii) a clearer definition for various game element …

Evaluating Electrification Of Fossil Fuel-Fired Boilers For Decarbonization Using Discrete Event Simulation, Nahian Ismail Chowdhury

Graduate Theses, Dissertations, and Problem Reports (ETD)

Decarbonizing fossil fuel usage is crucial in mitigating the impacts of climate change. CO2, which comprises the major portion of greenhouse gas, is emitted from burning fossil fuels. One of the significant sources of fossil fuel user is industrial process heating, and most of the heating in industrial processes is achieved through boilers. Electrification is a promising solution for decarbonizing these boilers, as it enables renewable energy sources to generate electricity, which can then be used to power the electric boilers. The electrification of boilers can reduce greenhouse gas emissions, improve air quality, and increase energy efficiency. However, it requires …

Dynamic Modeling, Data Reconciliation, Parameter Estimation, And Health Monitoring Of A Supercritical Power Plant, Katherine Grace Hedrick

Graduate Theses, Dissertations, and Problem Reports (ETD)

With the introduction of a larger portion of renewable sources of power coming onto the U.S. power grid in recent decades, the operational strategy of coal-fired power plants has changed significantly to focus more on flexibility in response to the changing energy market. This has naturally led to different operational challenges. Many of these challenges are focused on the boilers within these plants, as they are producing more emissions and experiencing increased damage during load-following, which in turn leads to increased costs from penalties for not achieving emission standards or maintenance costs as boilers accumulate damage from the cycling behavior. …

Spatio-Temporal Deep Learning Approaches For Addressing Track Association Problem Using Automatic Identification System (Ais) Data, Md Asif Bin Syed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the …

Framework For Data Acquisition And Fusion Of Camera And Radar For Autonomous Vehicle Systems, Clay Edward Vincent

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary contribution is the development of the data collection testing methodology for autonomous driving systems of a hybrid electric passenger vehicle. As automotive manufacturers begin to develop adaptive cruise control technology in vehicles, progress is being made toward the development of fully-autonomous vehicles. Adaptive cruise control capability is classified into five levels defined by the Society of Automotive Engineering. Some vehicles under development have attained higher levels of autonomy, but the focus of most commercial development is Level 2 autonomy. As the level of autonomy increases, the sensor technology becomes more advanced with a sensor suite which includes radar, …

Simulated Annealing Heuristics For The Dynamic Generalized Quadratic Assignment Problem, Yugesh Dhungel

Graduate Theses, Dissertations, and Problem Reports (ETD)

The Dynamic Generalized Quadratic Assignment Problem (DGQAP) is the task of assigning a set of facilities to a set of locations in a multi-period planning horizon such that the sum of the transportation and assignment/reassignment costs is minimized. The facilities may have different space requirements, and the capacities of locations may vary during the multiple-period planning horizon. Also, multiple facilities may be assigned to each location without violating the space capacity of the location. This research presents a formulation and applications of DGQAP in various layout and assignment problems encountered in the literature. Two Simulated Annealing (SA) metaheuristics named SA …