Operations Research, Systems Engineering and Industrial Engineering Commons^™

Fusion Et Validation De Modèles Musculosquelettiques De La Colonne Lombaire Pour La Prévention Et La Réadaptation Des Travailleurs, Christian Larivière, Farshid Ghezelbash, Amir Eskandari, Denis Gagnon, Hakim Mecheri, Navid Arjmand, Aboulfazl Shirazi-Adl

Rapports de recherche scientifique

Les charges imposées sur la colonne vertébrale lombaire constituent un facteur de risque important pour les maux de dos. Connaître la distribution de ces forces internes lors d’activités de travail est nécessaire autant en prévention qu’en réadaptation des travailleurs. La modélisation musculosquelettique (MS) de la colonne vertébrale est la seule approche non invasive permettant d’estimer les charges sur les structures internes de la colonne.

Deux modèles ont été développés au Québec, soient un modèle par éléments finis (MEF) guidé par le mouvement et un autre guidé par des mesures électromyographiques (EMG) d’activation musculaire (modèle MEMG). Ces modèles sont donc sensibles …

Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri

Theses and Dissertations

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …

Large Retail Logistics Warehouse Execution System, Michael Broughton

Graduate Research Theses & Dissertations

In this study, integrated technical solutions designed for large retail logistics (LRL) material handling equipment (MHE) were integrated with advanced technical industry trends to create a warehouse execution/warehouse control system (WES/WCS) distribution center (DC) model. This model was executed using a distributor profitability framework. The resulting process metrics, financial elements, and forecasted financial drivers within the new model were tested against a standard warehouse management system (WMS) DC model without WES or integrated WCS technical solutions. The hypothesis was that using the WES/WCS DC model in automating store functionality would result in superior cost-effective solutions for the P&L/EBITDA lines of …

Investigating The Influence Of Container Design And A Bulge Reduction Technique In Corrugated Fiberboard Containers Under Static Compression, Jay Singh, Koushik Saha

Industrial Technology and Packaging

This study explored the bulge effect in corrugated fiberboard boxes caused by excessive weight, leading to compromised stacking strength and potential product damage. It investigated how container design, specifically the height variations of regular slotted containers (RSCs), affects bulging under compression and its vulnerability to environmental conditions. Testing met ASTM standards, with samples conditioned according to ASTM and TAPPI protocols, revealing that changes in box height influence bulge displacement. Subsequent research examined the impact of top-to-bottom static compression loads on bulging in tape-reinforced RSC designs, evaluating bulge reduction and compression strength across various tape placements and environments. Notably, previous studies …

The Hazard Prediction Problem, Mary E. Helander, Brendan Smith, Sylvia Charchut, Erika Swiatowy, Calvin Nau, Gregory Cavaretta, Timothy Schuler, Adam Schunk, Héctor Ortiz-Peña

Social Science - All Scholarship

This work formulates the hazard prediction problem while addressing the research question: Can machine learning create a model to automatically recognize patterns that correspond to hazard state conditions during a mission-critical operation? Supervised learning models were trained and tested on data observed from mission simulators, which allowed for safe observation of dynamic system states and undesirable casualty events. The prediction task was formulated as a binary classification problem, producing the probability of being in a hazard state at time t and providing situational awareness of a possible imminent loss. Several modeling architectures were investigated: neural networks, logistic regression, a support …

Advancing A Systems Perspective On Innovative Behavior, Stephen Demski

Doctoral Dissertations

"Engineering organizations pursue innovation in strategy, structure, processes, and the services and products offered to remain relevant and competitive. Identifying factors supporting or constraining innovative work behavior and recognizing the complexity of their interactions are vital to sustaining an innovative workforce, yet how factors interact has not been comprehensively studied.

Recognizing innovative work behavior as the output of a complex system of factors guided this study’s literature search that identified over one hundred individual, team, and organizational factors influencing innovative behavior, interviews of engineers to learn what factors are essential in their work environment, and Delphi survey to rank factors, …

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 …

Abmscore: A Heuristic Algorithm For Forming Strategic Coalitions In Agent-Based Simulation, Andrew J. Collins, Gayane Grigoryan

Engineering Management & Systems Engineering Faculty Publications

Integrating human behavior into agent-based models has been challenging due to its diversity. An example is strategic coalition formation, which occurs when an individual decides to collaborate with others because it strategically benefits them, thereby increasing the expected utility of the situation. An algorithm called ABMSCORE was developed to help model strategic coalition formation in agent-based models. The ABMSCORE algorithm employs hedonic games from cooperative game theory and has been applied to various situations, including refugee egress and smallholder farming cooperatives. This paper discusses ABMSCORE, including its mechanism, requirements, limitations, and application. To demonstrate the potential of ABMSCORE, a new …

Analysis Of Performance Deterioration During Prolonged Wakefulness, Aaron J. King

Open Access Master's Theses

In a variety of professions, personnel are asked to work extended shifts, straining themselves both physically and mentally. The strain in combination with the prolonged wakefulness can lead to mistakes on the job. This can be problematic for industries where mistakes are associated with high-risk consequences. A few professions that typically work in shifts include nurses, truck drivers, and oil-rig workers to name a few. Pertinent to this study is the performance of operators performing pilot related activities such as driving or monitoring; this is specifically interesting where the risk for failure can result in fatality or serious injury, such …

Training Practitioners For Real-Time Product Development Using Generative Artificial Intelligence, Antoine J. Jetter, Ameeta Agrawal, Dahm Mongkol Hongchai, Charles Weber, Yufei Tao

Engineering and Technology Management Faculty Publications and Presentations

A workshop to train practitioners in real-time new product development (NPD) using generative artificial intelligence (AI) was conducted. Three teams of graduate students in Engineering and Technology Management (many with work experience as managers and engineers) were given minimalist instructions to develop a product concept and a preliminary marketing plan for a product of their choosing within six hours using ChatGPT, search engines, and other Internet-based tools. The product was to be novel, and the plan was to include customer identification, market analysis, generating personas, and a 15-minute presentation at the end of the workshop. The goal was to 'inspire …

Traveling The Pathway To A Culturally Competent Health Care Provider: Incorporating Study Abroad Into Your Curriculum, A L. Johnston Green, C J. Heffner

Clinical Practice in Athletic Training

Introduction: As athletic training employment settings expand and diversify, so does the patient population with whom these healthcare providers interact. This growth furthers the need to ensure that students enrolled in an athletic training program (ATP) accredited by the Commission on Accreditation of Athletic Training Education (CAATE) are educated to provide culturally competent care. Culturally competent care is provided when decisions made diminish the inequalities that may be present in a system based upon a patient’s individual circumstances. Implementing cultural competency education may be integrated into athletic training education in various manners as necessitated by each institution’s unique circumstances. Programs …

An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of System of Systems programs such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question - can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the …

Navigating Innovation Within A Contractor's Business Model: A System Dynamics Approach, Mariam Elazhary, Cihan Dagli, Islam H. El-Adaway

Engineering Management and Systems Engineering Faculty Research & Creative Works

Innovation is essential in the construction industry, but current practices often focus narrowly on specific aspects rather than taking a comprehensive approach. To address this, systematic innovation through business model innovation (BMI) is proposed to enhance overall performance. Despite increasing interest in BMI research, it remains in its early stages, lacking quantitative analysis of its impact on business model components. This paper develops a framework using system dynamics to assess changes to a construction company's BM when faced with a variety of innovations across various projects. This shall be accomplished by (1) establishing factors and their relationships that define a …

A Systematic Review Of Phenotypic And Epigenetic Clocks Used For Aging And Mortality Quantification In Humans, Brandon Warner, Edward Ratner, Anirban Datta, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers, and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This …

A Model-Adaptive Random Search Actor Critic: Convergence Analysis And Inventory-Control Case Studies, Yuehan Luo, Jiaqiao Hu, Abhijit Gosavi

Engineering Management and Systems Engineering Faculty Research & Creative Works

Reinforcement learning (RL) is an exciting area within the domain of Markov Decision Processes (MDPs) in which the underlying optimization problem is solved either in a simulator of the real-world system or via direct interaction with the real-world system, when its underlying transition probabilities are difficult to estimate. The latter is commonly true of large-scale, real-world MDPs with complex underlying transition dynamics. RL is currently being widely researched in the world of medicine/neuroscience after some spectacular success stories demonstrating super-human behavior in computer games. In this paper, we propose a new actor-critic-based RL algorithm for approximately solving continuous state/action MDPs …

A Simulation-Based Digital Twin For Data-Driven Maintenance Scheduling Of Risk-Prone Production Lines Via Actor Critics, Abhijit Gosavi, Aparna Gosavi

Engineering Management and Systems Engineering Faculty Research & Creative Works

Industry 4.0 mandates a shift from traditional total productive maintenance (TPM) methods, which rely on periodic data gathering and subsequent offline modeling for maintenance scheduling, towards more data-driven and online decision-making approaches. Further, Industry 4.0 emphasizes reducing intervention from high-skilled managers and leveraging real-time data for decision-making, which is characteristic of models rooted in either renewal-theoretic or Markov chains for traditional TPM methods. Digital Twins (DTs), which are virtual representations of physical systems, play a crucial role in this paradigm by enabling online decision-making of maintenance scheduling directly at the workstation level. In this paper, a simulation-based DT (S-DT) is …

The Impact Of Journalistic Cultures On Social Media Discourse: Us Primary Debates In Cross-Lingual Online Spaces, Lea Hellmueller, Lindita Camaj, Sebastián Vallejo Vera, Peggy Lindner

Engineering Management and Systems Engineering Faculty Research & Creative Works

This cross-lingual project examines how social media posts of Spanish- and English-language media impact incivility in user comments during the 2020 primary political debates in the United States. We analyzed Facebook posts of news organizations that hosted the debates and used a state-of-the-art machine-learning model to analyze the corresponding comments. Our findings reveal distinct journalistic cultures on the post-level: English-language media are significantly more likely to use interpretation while Spanish-language media employ more audience-engagement and factual reporting strategies. We argue that in order to understand incivility in social media discourse during political debates, we need to consider journalistic cultures: While …

The Precedence-Constrained Quadratic Knapsack Problem, Changkun Guan

Honors Theses

This thesis investigates the previously unstudied Precedence-Constrained Quadratic Knapsack Problem (PC-QKP), an NP-hard nonlinear combinatorial optimization problem. The PC-QKP is a variation of the traditional Knapsack Problem (KP) that introduces several additional complexities. By developing custom exact and approximate solution methods, and testing these on a wide range of carefully structured PC-QKP problem instances, we seek to identify and understand patterns that make some cases easier or harder to solve than others. The findings aim to help develop better strategies for solving this and similar problems in the future.

Project Control System (Pcs) Implementation In Engineering And Construction Projects: An Empirical Study In Saudi's Petroleum And Chemical Industry, Sahar Jawad, Ann Ledwith, Rashid Khan

School of Cybersecurity Faculty Publications

Purpose

There is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and successfully achieving project objectives has yet to be explored. This research investigates the enablers and barriers that influence the elements of PCS success and drive project objectives.

Design/methodology/approach

This study adopts a mixed approach of descriptive analysis and regression models to explore the impact of six PCS elements on project outcomes. Petroleum and chemical projects in Saudi Arabia were selected as a case study to validate the research model.

Findings

Data from a …

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 …

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 …

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 …

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 …

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 …

Yankee Manufacturing Multi-Assembly Pvc-Gasket Assembly Mechanism, Lily Coss, Jessica Kun, Dana Chapin, Jenan Hasan

Williams Honors College, Honors Research Projects

This project focuses on designing and implementing an efficient mechanism to connect PVC and gasket pieces in the door assembly process at Yankee Manufacturing. The aim is to improve process efficiency and productivity by increasing the assembly rate from 130 to 195 assemblies per hour, a 38% improvement. The project involved research, design iterations, performance evaluations, and adherence to engineering codes and standards, particularly focused on safety, ergonomics, and sustainability. The design process includes addressing challenges from a previous attempt, redefining project scope, and incorporating new elements such as transitioning to the packaging process seamlessly. Ethical considerations include promoting operator …

Letac-Mpc: Learning Model Predictive Control For Tactile-Reactive Grasping, Zhengtong Xu, Yu She

School of Industrial Engineering Faculty Publications

Grasping is a crucial task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects under various conditions and with differing physical properties. In this article, we introduce LeTac-MPC, a learning-based model predictive control (MPC) for tactile-reactive grasping. Our approach enables the gripper to grasp objects with different physical properties on dynamic and force-interactive tasks. We utilize a vision-based tactile sensor, GelSight (Yuan et al. 2017), which is capable of perceiving high-resolution tactile feedback that contains information on the physical properties and states of the grasped object. LeTac-MPC incorporates a differentiable MPC layer designed to model …

Leto: Learning Constrained Visuomotor Policy With Differentiable Trajectory Optimization, Zhengtong Xu, Yu She

School of Industrial Engineering Faculty Publications

This paper introduces LeTO, a method for learning constrained visuomotor policy with differentiable trajectory optimization. Our approach integrates a differentiable optimization layer into the neural network. By formulating the optimization layer as a trajectory optimization problem, we enable the model to end-to-end generate actions in a safe and constraint-controlled fashion without extra modules. Our method allows for the introduction of constraint information during the training process, thereby balancing the training objectives of satisfying constraints, smoothing the trajectories, and minimizing errors with demonstrations. This “gray box” method marries optimization-based safety and interpretability with powerful representational abilities of neural networks. We quantitatively …

User-Interaction With A Web-Served Global Ground Motion Relational Database, T. E. Buckreis, C. C. Nweke, P. Wang, S. J. Brandenberg, R. Shams, M. E. Ramos-Sepulveda, R. Pretell, S. Mazzoni

Civil & Environmental Engineering Faculty Publications

We present an application programming interface (API) which facilitates public access to a global relational database of earthquake ground motion intensity measures, associated metadata, and time-series data. Next Generation Attenuation (NGA)-East and NGA-West2 project spreadsheets have been adapted into a relational database format composed of multiple tables through a series of primary and foreign keys. The combined dataset has been expanded to include contributions from earthquakes, generally with magnitudes greater than M3.9, that have occurred since the conclusion of the data synthesis component of both projects in 2011. Currently the database includes 62,449 ground motions recorded at 9,092 stations for …

Yankee Feedstock Project, Nathan Mcanany, Richard Gualtiere

Williams Honors College, Honors Research Projects

This report details the design process, application, considerations, costs, and functionality of an automated tape “feedstock” machine. Yankee Insulation Products will use this project to create rolls of Aluminum foil tape that are included in their Therma-Dome assembly kit. The goal of this design will aid in the streamlining and effectiveness of the current process. When designing the automated process, the current process was evaluated for areas of improvement. Design parameters were also given to the project by the sponsoring company that were to be considered and incorporated.

Conflict Profiles And Team Outcomes In Cross-Disciplinary Teams: An Integrated Latent Profile Analysis And Natural Language Processing Approach, Francisco Cima, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

Team conflict is a naturally emerging phenomenon resulting from individuals' interactions during project execution. Cross-disciplinary teams can experience higher levels of conflict than single-discipline teams because of the increased diversity of knowledge and perspectives. Research has shown that team conflict can emerge from different types of disagreements (cognitive and interpersonal), which have different implications for team functioning. Past empirical research has focused on the impact of both conflict types independent from each other while overlooking their combined effects. This work examines the conflict profiles resulting from the combined levels of interpersonal and cognitive disagreements and their association with team outcomes. …