Operations Research, Systems Engineering and Industrial Engineering Commons^™

Systematic Analysis Of Fatal Collisions Between Mobile Equipment And Pedestrian Workers To Inform The Use Of Proximity Detection Devices, Damien Burlet-Vienney, François Gauthier, Chantal Gauvin

Articles dans des actes de congrès

Context.

Collisions between mobile industrial equipment and pedestrian workers (PWs) are a major occupational health and safety concern, particularly on construction sites (e.g., during earth-moving operations). The use of driver aids, such as proximity warning devices designed to alert workers and drivers of potential collisions, is a growing and constantly developing avenue of prevention (e.g., AI-enhanced cameras; RFID tags). However, structured feedback to guide the implementation of these technologies on mobile equipment remains rare (e.g., technology choice, installation and settings).

Objective/Methodology.

 In this context, a systematic analysis of all fatal accident reports related to this issue for the province of …

How Inclusive Are Degree Programs In Operations Research And Analytics In The U.S.?, Michael P. Johnson Jr., Tayo Fabusuyi, Elham Hesari, Salina Chu

Public Policy and Public Affairs Faculty Publication Series

Academic programs in colleges and universities in the United States have increasingly embraced principles of equity and inclusion to support student success and diversity within professions. Innovations in diversity, equity, and inclusion (DEI) in university programs require baseline data on students, faculty and administrators, curricula, and programmatic support. A review of data shows that underrepresented groups in the US workforce have limited visibility in operations research and analytics. A review of DEI-related research reveals some relevant insights including a gap in studies specific to operations research and analytics.

To provide evidence in support of DEI initiatives in OR/analytics, we describe …

Mitigating Out-Of-Plane Fiber Waviness In Afp Laminates With Tow-Gaps Via Selective Placement Of Thermoplastic Veils, Ahmadreza Ravangard, Kuthan Celebi, Sergii G. Kravchenko, Oleksandr G. Kravchenko

Mechanical & Aerospace Engineering Faculty Publications

Fiber tow-gaps and overlaps formed during the Automated Fiber Placement (AFP) process pose a significant challenge by introducing non-uniform composite morphologies, often characterized by resin-rich regions and fiber waviness. These defects occur as deposited fibers sink into the gap regions during consolidation, with gap geometry determined during path planning. Such morphological inconsistencies can compromise structural reliability by initiating premature failure, particularly through localized out-of-plane waviness and resin accumulation. This study investigates the integration of high melting temperature thermoplastic veils, specifically polyetherimide (PEI), into fiber tow-gaps as a method to prevent ply sinking and reduce fiber waviness on both internal and …

Fetal Acidosis Prediction Using Attention Enhanced Convolutional Neural Networks, Anusha Adhikari

Masters Theses

This study explores the integration of spectral mixtures of fetal heart rate (FHR) and uterine contraction (UC) signals to enhance the prediction of fetal acidosis, utilizing the CTU-CHB dataset. Several classification models were trained using two distinct oversampling techniques and inputs, demonstrating that models incorporating spectral mixtures significantly outperform those using raw signals. These models, particularly when combined with convolutional neural networks (CNNs) and attention mechanisms, achieved a notable F1-score of 0.98, with the highest model achieving an area under the Receiver Operating Characteristic (ROC) curve of 0.95. The research employs a variety of techniques including short-time Fourier transform and …

Long Short-Term Memory (Lstm) -Based Neural Network Model For Optimizing Composite Manufacturing Process Using Autoclave, Sourav P. Bolar

Masters Theses

The combination of high pressure and controlled heat plays a critical role in ensuring the uniform curing of composite materials, leading to parts with superior mechanical properties. In this study, three composite samples of IM7/CYCOM 5320-1, each cut into 12x12-inch squares, were placed in an autoclave at three different locations, spaced 6 inches apart. Sixteen thermocouples were randomly distributed across the setup to monitor the curing process as the autoclave temperature was systematically ramped up and down while maintaining constant pressure, creating a fully controlled curing environment. The primary objective was to optimize the curing locations to reduce machine runtime …

Application Of Artificial Intelligence Techniques To Improve Leadership Decision Making With Uncertainty, Michael David Parrish

Doctoral Dissertations

"Every good leader is a good manager, but not every good manager is a good leader. The difference between the leader and the manager is critical decision-making. Today’s decision-making environment is characterized as Volatile, Uncertain, Complex, and Ambiguous (VUCA). With the exponential increase in the technical capabilities of systems, the human has become the weakest link in the use of such systems. To remain relevant, good leaders must continuously adapt to new advances in technology and processes.

The research contributions of this work provide several unique and novel solutions for leaders to utilize artificial intelligence tools to improve and optimize …

A Comprehensive Analysis Of Climate Resilience Strategies For Small Island Developing States, Ashley-Ann Davis

Doctoral Dissertations

Small Island Developing States (SIDS) face unique and disproportionate challenges in their efforts to achieve climate resilience due to their geographic vulnerabilities, limited resources, and systemic economic and social constraints. This dissertation provides a comprehensive analysis of climate resilience strategies tailored to SIDS, addressing critical systemic issues and exploring interdisciplinary solutions. It synthesizes findings across four interconnected studies, including an analysis of disaster preparedness and response mechanisms in developing countries to identify gaps and opportunities for improving resilience in extreme events. The research investigates economic factors influencing energy portfolio transitions in the Caribbean, emphasizing the complexities of renewable energy adoption. …

Deep Learning Architecture Design For Nano-Satellite Image Super-Resolution, William Everette Symolon

Doctoral Dissertations

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. NanoSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation, and research. They also have the potential to enhance satellite constellation resilience. However, the inherent size, weight, and power limitations of NanoSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution, which limits the utility of CubeSat images for military planning purposes and national intelligence applications. This research proposed a deep learning architecture capable of enhancing …

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

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 …

Thermal Management For Optimal Performance Of Polymer Electrolyte Membrane Unitized Regenerative Fuel Cells, Mythy Tran, Ayodeji Demuren

Mechanical & Aerospace Engineering Faculty Publications

Hydrogen is an excellent carrier for energy storage and can be produced from various green and renewable sources. However, the cost of producing hydrogen and converting it to useful energy is much higher than fossil fuel and traditional energy generation and storage systems. Unitized regenerative fuel cells (URFC) maximize utilization of high-cost cells and their components, thus, lowering system capital cost. Improving the URFC efficiency is an effective way to lower its operating cost. This study evaluates utilization of waste heat during operation and recovery strategy to improve system efficiency of Proton Exchange Membrane (PEM) URFC. A COMSOL Multiphysics 3-D …

A Systematic Literature Review On Resilient Digital Transformation, Examining How Organizations Sustain Digital Capabilities, Thira Chavarnakul, Li Da Xu, Zhuming Bi, Achyut Shankar, Gaurav Dhiman, Wattana Viriyasitavat, Danupol Hoonsopon

Information Technology & Decision Sciences Faculty Publications

In an era marked by relentless technological shifts and market volatility, digital transformation (DT) alone is insufficient. Organizations must develop Resilient Digital Transformation (RDT)—the organizational capabilities required to sustain DT over a medium-term horizon—to navigate these challenges effectively. This study primarily aims to propose a guideline for fostering RDT. Drawing on the PRISMA guidelines and a systematic review of 77 peer-reviewed papers, this study identifies and synthesizes key targets and drivers across three core pillars: Technology, Organization, and External Environment. These elements collectively foster organizational resilience. Specifically, this study highlights how adaptability, innovation, and scalability form the technological underpinnings of …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

A System To Test Selective Directional Laser Melting Of 3d Printed Metal Surfaces, Daniel A. Marasco

Open Access Master's Theses

This thesis contains Controlled Unclassified Information (CUI). CUI has been removed from this copy of the thesis.

Machining-Induced Residual Stress Modeling Using Physics-Informed Neural Networks, Md Mehedi Hasan

Theses and Dissertations--Mechanical and Aerospace Engineering

Process-induced residual stress (RS) plays a critical role in determining the structural integrity, fatigue life, corrosion resistance, and dimensional stability of manufactured components. Tensile residual stress can be harmful to the machined workpiece by lowering fatigue strength, while compressive residual stress helps to prevent such issues. Therefore, accurate prediction and control of machining-induced residual stresses are essential for optimizing manufacturing processes and enhancing component performance. However, existing modeling approaches—empirical, analytical, and numerical—often have limited predictive accuracy, demand extensive calibration, or involve high computational costs. Traditional finite element analysis (FEA) provides modeling insights but is constrained by its high computational cost …

Integrated Compliant Structure For A Hand Exoskeleton, Tristan R. Koopman

Honors Undergraduate Theses

This thesis presents the design and prototyping of a wearable hand exoskeleton that integrates a flexible structural framework to assist with hand movement while maintaining comfort and anatomical conformity. The goal was to create a device that supports tendon-driven actuation through a compliant structure, combining elements of rigidity and flexibility to match the natural geometry and motion of the human hand. Traditional hand exoskeletons often trade off motion for structure or vice versa. This project aims to bridge that gap with a hybrid compliant design that balances flexibility and support. The design process followed an iterative approach involving rapid prototyping …

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

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 …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, Durga C. Panigrahi, Cihan H. Dagli, A. Singh

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Data-Driven Layout Design For Smart Remanufacturing: A Flexible Optimization Model And A Case Study, J. A. Afari, A. Gosavi, J. Hu, R. J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Abstract: In remanufacturing, a vital segment of the sustainable, low-carbon circular economy, existing versions of the traditional unequal-areas facility layout problem (UA-FLP) model face significant limitations in designing layouts. To be specific, in the process of minimizing the material-handling cost (MHC), these models also alter departmental dimensions, often diverging from construction specifications. This poses a difficulty, as critical equipment required for remanufacturing, e.g., sorting and cleaning machines, have unalterable dimensions, which implies that departmental dimensions cannot be changed from specifications provided. To address this, a novel Flexible Envelope UA-FLP (FE-UA-FLP) model is proposed in this work for designing layouts wherein …

Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

AI-driven healthcare decision-making is multi-faceted, requiring complex logic to adapt to evolving policies and societal demands. Effective change implementation by healthcare providers and multidisciplinary organ transplant teams depends on adaptive decision-making. The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) model introduces a novel approach to Human-AI Teaming, keeping human expertise central while dynamically adjusting to changing requirements. TSFAM employs fuzzy logic to manage imperfect data and human ambiguity, integrating the transplant surgeon perspective with the AI deep learning decision-making tool, creating a resilient solution in this critical domain. By embedding adaptive capabilities into the architecture, TSFAM exemplifies the adaptability of …

Electricity Theft Detection With An Adaptive Deep Learning Architecture, Mohammed Sleiman, Cihan Dagli, Rui Bo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Electricity theft presents a significant challenge to the power industry. This paper demonstrates an adaptive deep framework integrating dimensionality reduction, graph modeling, attention mechanisms, and dynamic feature refinement for improving theft detection. Principal Component Analysis squeezes consumption data while an Autoencoder extracts latent representations and denoises the input. A Gated Graph Convolutional Neural Network uses k-Nearest Neighbors to model local relationships, while Transformers capture long range global dependencies. Neural Ordinary Differential Equations then refine features over continuous time, improving adaptability to complex patterns. The framework achieves 94.01% accuracy with stratified 5-fold cross validation. However, class imbalance challenges the minority class …

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

Molecular Dynamics-Based Two-Dimensional Simulation Of Powder Bed Additive Manufacturing Process For Unimodal And Bimodal Systems, Yeasir Mohammad Akib, Ehsan Marzbanrad, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

The trend of adapting powder bed fusion (PBF) for product manufacturing continues to grow as this process is highly capable of producing functional 3D components with micro-scale precision. The powder bed’s properties (e.g., powder packing, material properties, flowability, etc.) and thermal energy deposition heavily influence the build quality in the PBF process. The packing density in the powder bed dictates the bulk powder behavior and in-process performance and, therefore, significantly impacts the mechanical and physical properties of the printed components. Numerical modeling of the powder bed process helps to understand the powder spreading process and predict experimental outcomes. A two-dimensional …

Vilp: Imitation Learning With Latent Video Planning, Zhengtong Xu, Qiang Qiu, Yu She

School of Industrial Engineering Faculty Publications

In the era of generative AI, integrating video generation models into robotics opens new possibilities for the general-purpose robot agent. This letter introduces imitation learning with latent video planning (VILP). We propose a latent video diffusion model to generate predictive robot videos that adhere to temporal consistency to a good degree. Our method is able to generate highly time-aligned videos from multiple views, which is crucial for robot policy learning. Our video generation model is highly time-efficient. For example, it can generate videos from two distinct perspectives, each consisting of six frames with a resolution of 96 × 160 pixels, …

Unit: Data Efficient Tactile Representation With Generalization To Unseen Objects, Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang, Cael Fitch, Philip Glen Crandall, Wan Shou, Dongyi Wang, Yu She

School of Industrial Engineering Faculty Publications

UniT is an approach to tactile representation learning, using VQGAN to learn a compact latent space and serve as the tactile representation. It uses tactile images obtained from a single simple object to train the representation with generalizability. This tactile representation can be zero-shot transferred to various downstream tasks, including perception tasks and manipulation policy learning. Our benchmarkings on in-hand 3D pose and 6D pose estimation tasks and a tactile classification task show that UniT outperforms existing visual and tactile representation learning methods. Additionally, UniT's effectiveness in policy learning is demonstrated across three real-world tasks involving diverse manipulated objects and …

Vibtac: A High-Resolution High-Bandwidth Tactile Sensing Finger For Multi-Modal Perception In Robotic Manipulation, Sheeraz Athar, Xinwei Zhang, Jun Ueda, Ye Zhao, Yu She

School of Industrial Engineering Faculty Publications

Tactile sensing is pivotal for enhancing robot manipulation abilities by providing crucial feedback for localized information. However, existing sensors often lack the necessary resolution and bandwidth required for intricate tasks. To address this gap, we introduce VibTac, a novel multi-modal tactile sensing finger designed to offer high-resolution and high-bandwidth tactile sensing simultaneously. VibTac seamlessly integrates vision-based and vibration-based tactile sensing modes to achieve high-resolution and high-bandwidth tactile sensing respectively, leveraging a streamlined human-inspired design for versatility in tasks. This paper outlines the key design elements of VibTac and its fabrication methods, highlighting the significance of the Elastomer Gel Pad (EGP) …

The Evolving Use Of Strategic Planning Tools In The Manufacturing Environment: Implications For Quality 4.0 And Beyond, Richard Lee Wilson

Doctoral Dissertations

"Having a strong strategic plan is critical for success for any business no matter the size of the organization, the product or service they provide, or the industry they serve. There are many methods businesses use to develop their strategic plans. One such method is known as Hoshin Kanri, which has been in use for decades. However, recent years have seen an increase in artificial intelligence, big data, data analytics, and other technology tools to create cyber physical systems on the manufacturing floor. The increase in technology in manufacturing to integrate cyber systems with physical systems spawned a new industrial …