Industrial Engineering Commons^™

Bridging Models And Real-World Performance: Capturing Represented, Hidden, And Unrepresented Dynamics In Complex Systems, Dixizi Liu

All Dissertations

Complex systems often depend on adaptive performance that is difficult to capture using structured data or formal models alone. This dissertation asks how predictive modeling, coordination-cost measurement, and simulation can be integrated to better represent the visible, hidden, and unrepresented dynamics that shape real-world system performance.

Sterile Processing Departments (SPDs) provide the empirical context for this work because they support safe and timely surgical care while operating under high workload variability, time pressure, and tight coordination with operating rooms. Hospitals rely on SPDs to clean, assemble, sterilize, and deliver surgical instrument trays so surgeries can start on time and proceed …

A Hybrid Machine Learning-Based Feasibility Prediction Of 3d Mechanical Designs Using Scalar And Geometric Features, Md Mohsin Uddin Fahim

Open Access Theses & Dissertations

The computational bottleneck of structural feasibility screening frequently hinders the transition from a digital 3D model to a physically manufactured component. Traditionally, engineers have relied on either overly rigid heuristic constraint checks or computationally exhaustive finite element simulations. To address this inefficiency, this thesis proposes and validates a hybrid machine-learning framework to predict the structural manufacturability of 3D mechanical designs. Moving beyond the conventional reliance on isolated scalar parameters, the proposed methodology extracts and integrates both scalar manufacturing constraints (e.g., tolerance, minimum feature thickness) and spatial geometric descriptors (e.g., bounding volume, aspect ratio) directly from STL mesh data. Utilizing a …

Data Driven Estimation Of Pore Size Using 1d Light Emissions For Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jorge Barron, Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

The quality assurance of the Laser Powder Bed Fusion Process (LPBF) has been extensively investigated over the last decade for in-situ monitoring of metal additive manufacturing. The process inherently generates voids within the bulk of the part, which can detrimentally affect the quality of the printed part. The characterization of these voids by estimating their size and identifying their geometrical features remains a challenge. This study introduces a Machine Learning (ML) based framework for estimating void sizes of varying geometries using layer-wise one-dimensional (1D) average light intensity signal obtained from the optical tomography system during the 3D printing of metallic …

Transparent Eeg Analysis: Leveraging Autoencoders, Bi-Lstms, And Shap For Improved Neurodegenerative Diseases Detection, Badr Mouazen, Ahmed Bendaouia, Omaima Bellakhdar, Khaoula Laghdaf, Aya Ennair, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Highlights

• Novel hybrid architecture: Combined autoencoders with bidirectional LSTM networks for enhanced EEG signal classification, achieving 98% accuracy in distinguishing AD, FTD, and healthy controls.

• Explainable AI integration: Implemented SHAP (SHapley Additive exPlanations) framework to enhance model transparency and identify entropy as the most influential feature for neurodegenerative disease detection.

• Optimal temporal segmentation: Demonstrated that 5-s EEG windows with 50% overlap provide the best balance between classification accuracy and computational efficiency.

• Comprehensive feature extraction: Utilized Power Spectral Density (PSD) analysis across standard frequency bands (Delta, Theta, Alpha, Beta, Gamma) following autoencoder-based dimensionality reduction.

• Superior performance validation: Outperformed traditional machine learning …

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A Machine Learning Approach To Detect Pores In Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jose Barron Jr., Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

Real-time detection of pores in the Laser Powder Bed Fusion (LPBF) metal Additive Manufacturing (AM) process is proposed in this study and can be utilized for in-situ process monitoring and quality control. The average light emission data from the process captured by an optical tomography camera can be integrated into a defect detection module to characterize defects after the deposition of a layer. The light emission contains information on the process zone which could be extracted with the appropriate data techniques. In this paper, we proposed a machine-learning approach that utilizes the mean light intensity data from the melt-pool monitoring …

Machine Learning And Clinical Eeg Data For Multiple Sclerosis: A Systematic Review, Badr Mouazen, Ahmed Bendaouia, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and …

Data-Driven Dynamic Decision-Making Using Discrete Optimization And Supervised Machine Learning, Navid Rashedi

Dartmouth College Ph.D Dissertations

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal …

A Human-In-The-Loop Framework For Scalable And Interpretable Event Triaging In Large-Scale Systems, Ibrahim Khaled Al-Agha

Doctoral Dissertations

This dissertation presents a comprehensive and scalable framework for real-time fault detection and event triage in industrial systems, addressing critical challenges such as class imbalance, ambiguous feature boundaries, and the prioritization of complex, high-dimensional event data. The proposed framework integrates advanced methodologies, including micro-batch processing, retrospective divergence-based event detection (DB-RED), association rule mining (ARM), clustering, and Dempster-Shafer Theory (DST) for conflict resolution. Together, these components enable the systematic stratification of events into actionable priority levels, ensuring robust and interpretable decision-making in real-time environments. DB-RED forms the cornerstone of the framework, leveraging KL-divergence and PE-divergence metrics to detect subtle and transient …

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 …

Data-Driven Roughness Estimation Of Additively Manufactured Samples Using Build Angles, Jose Galarza, Jose Barron Jr., Farid Ahmed, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

Achieving control of Laser Powder Bed Fusion (L-PBF) over the quality of the print is the main motivation for finding an optimum set of parameters in the process. Surface roughness is one of the characteristics of the print that impacts the performance of the desired functionality. This research focus is to relate the build angle with the surface roughness on the L-PBF printed specimens and utilize machine learning methods for roughness estimation of geometric features with varying build angles. The EOS M290 L-PBF printer was used to print Inconel-718 coupons using standard process parameters while varying build angles from 20 …

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 …

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 …

Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors In Mineral Processing, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Oumkeltoum Amar, Mohamed Chekroun, Oussama Hasidi, Oussama Lachihab

Manufacturing & Industrial Engineering Faculty Publications

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth …

Hybrid Features Extraction For The Online Mineral Grades Determination In The Flotation Froth Using Deep Learning, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Abderrahmane Benhayoun, Oumkeltoum Amar, François Bourzeix, Karim Baïna, Mouhamed Cherkaoui, Oussama Hasidi

Manufacturing & Industrial Engineering Faculty Publications

The control of the froth flotation process in the mineral industry is a challenging task due to its multiple impacting parameters. Accurate and convenient examination of the concentrate grade is a crucial step in realizing effective and real-time control of the flotation process. The goal of this study is to employ image processing techniques and CNN-based features extraction combined with machine learning and deep learning to predict the elemental composition of minerals in the flotation froth. A real world dataset has been collected and preprocessed from a differential flotation circuit at the industrial flotation site based in Guemassa, Morocco. …

Application Of Learning Processes For Improving Last-Mile Logistics Optimization At Scale, Seyedeh Shaghayegh Rabbanian

LSU Doctoral Dissertations

The escalating demands of omnichannel retailing, rapid urbanization and shifting customer behaviors have propelled last-mile vehicle routing logistics to the forefront of research. This last-mile phase, recognized as a significant contributor to costs and pollution in the supply chain, necessitates efficient route optimization to minimize expenses and environmental impact. This research delves into machine learning based techniques for solving large-scale Vehicle Routing Problem (VRP), a fundamental concern in last-mile logistics, aiming to optimize delivery vehicle routing amidst diverse customer nodes and operational constraints. Three primary research subproblems are analyzed: utilizing machine learning for constructive solutions, Variable Neighborhood Search (VNS) metaheuristic, …

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 …

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 …

Employment Of Intelligent Predictive Maintenance On Thermal Power Plant Component Parts Taking Condenser Vacuum As A Case Study, Titus Ajewole

Tanzania Journal of Engineering and Technology (TJET)

This work proposes deployment of machine learning in the maintenance of individual constituent parts of steam power plant assemblages. With the condenser vacuum of a steam turbine (in a six-turbine plant assemblage) taken as a case study, information on the past operating parameters of the selected plant component was used to forecast its future working condition. Based on Exponential Gaussian Process of Regression, a model was developed, trained using the diachronic operational data, and employed in determining the future. A quantitative evaluation was employed to provide the distribution of the test values of the data about the lines of regression, …

Assessing And Predicting The Students’ Systems Thinking Preference: Multi-Criteria Decision Making And Machine Learning, Siham Tazzit

Theses and Dissertations

The 21st century is marked by a technological revolution that features digital implementation and high interconnectivity between systems across different domains, such as transportation, agriculture, education, and health. Although these technological changes resulted in modern systems capable of easing individuals’ lives, these systems are increasingly complex, and that increased complexity is only expected to continue. The increased system complexity is due to the rapid exchange of information between subsystems, which creates high interconnectivity and interdependence between the subsystems and their elements. Workforce skill sets, as a result, must be modified appropriately to ensure the systems’ success. Systems Thinking is an …

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 …

Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez

Senior Design Project For Engineers

Family Restaurant is a local restaurant in the greater Atlanta area that serves a variety of dishes that include an assortment of 19 different proteins. Currently, Family Restaurant places protein orders based on business intuition, and tends to over-stock and sometimes under-stock. To minimize inventory costs by reducing over-stocking and preventing under-stocking of proteins, we applied Facebook Prophet (FB Prophet), ARIMA, and XG Boost machine learning models to predict protein demand and then fed these results into a Fixed Time Period inventory model to make an overall order suggestion based on the specified time period. We trained our models on …

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

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 …

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. …

Predicting The Likelihood And Scale Of Wildfires In California Using Meteorological And Vegetation Data, Matthew Walters

Graduate Theses and Dissertations

Wildfires have devastating ecological, environmental, economical, and public health impacts through the deterioration of water and air quality, CO2 emissions, property damage, and lung illnesses. The early detection and prevention of wildfires allow for the minimization of these risks. The use of Artificial Intelligence (AI) in wildfire detection and prediction has been highly researched as a tool to assist firefighters in stopping wildfires in its early stages. The three common wildfire prediction categories include image and video detection, behavior prediction, and susceptibility prediction. Data such as climate, weather, vegetation, satellite images, and historical wildfire data is most commonly used. Many …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

A Brief Literature Review For Machine Learning In Autonomous Robotic Navigation, Jake Biddy, Jeremy Evert

Student Research

Machine learning is becoming very popular in many technological aspects worldwide, including robotic applications. One of the unique aspects of using machine learning in robotics is that it no longer requires the user to program every situation. The robotic application will be able to learn and adapt from its mistakes. In most situations, robotics using machine learning is designed to fulfill a task better than a human could, and with the machine learning aspect, it can function at the highest level of efficiency and quality. However, creating a machine learning program requires extensive coding and programming knowledge that can be …

Improving Or Operations Using Machine Learning Techniques, Tannaz Khaleghi

Wayne State University Dissertations

Recently, health care related studies are being widely conducted by researchers using unique and efficient techniques to increase system profitability, quality of care, and patient satisfaction. Surgery department is considered as the hospital's engine, and cost of surgical services has a huge impact on the overall profitability of the hospital. This thesis proposes novel approaches to improve the efficiency of surgical services by using machine learning concepts.

In the first part, this research investigates the prediction of the surgery durations and Current Procedural Terminology (CPT) Codes. Accurate prediction of the surgery duration will improve the utilization of indispensable surgical resources …

Using A Systemic Skills Model To Build An Effective 21st Century Workforce: Factors That Impact The Ability To Navigate Complex Systems, Morteza Nagahi

Theses and Dissertations

The growth of technology and the proliferation of information made modern complex systems more fragile and vulnerable. As a result, competitive advantage is no longer achieved exclusively through strategic planning but by developing an influential cadre of technical people who can efficiently manage and navigate modern complex systems. The dissertation aims to provide educators, practitioners, and organizations with a model that helps to measure individuals’ systems thinking skills, complex problem solving, personality traits, and the impacting demographic factors such as managerial and work experience, current occupation type, organizational ownership structure, and education level. The intent is to study how these …

Machine Learning Models For Lodi Indices., Lucas A. Bruns

Electronic Theses and Dissertations

Two indices published monthly by the Logistics and Distribution Institute (LoDI) predict changes in logistics and distribution activity levels nationally and regionally and are useful for organizations when planning projects and expenses. This research validates the current linear regression model, updates the index conversion method, and introduces machine learning models.

New source data are introduced to the models to validate the current linear regression model and a comparative analysis verifies that the current source data are robust. A rolling average is used for index conversion in place of a fixed reference month to reflect recent changes in employment levels.

Three …