Industrial Engineering Commons^™

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Functionally Magnetic Gradient Copper-Nickel Material Fabricated Via Directed Energy Deposition, Vy Tran Phuong Nguyen

Graduate Theses, Dissertations, and Problem Reports (ETD)

Functionally gradient materials (FGMs) of CuSn10 and Inconel 718 were fabricated via a hybrid directed energy deposition (DED) system. The objective of the present thesis is to determine the feasibility of manufacturing CuSn10 and Inconel 718 FGMs via DED and investigate the physical and mechanical properties and the microstructures of the resulting FGMs. The physical tests comprised of conductivity and Seebeck coefficient measurements. The microstructure analysis and mechanical testing include microscopic imaging, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and hardness test. In addition, compressive strength test was performed to analyze the interface bonding behaviors.

Developing Artificial Intelligence Tools To Investigate The Phenotypes And Correlates Of Chronic Kidney Disease Patients In West Virginia, Marzieh Amiri Shahbazi

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Developing Artificial Intelligence tools to investigate the phenotypes and correlates of Chronic Kidney Disease patients in West Virginia

Marzieh Amiri Shahbazi

Chronic kidney disease (CKD) is responsible for disrupting the lives of 37 million people just in the USA, which is about 1 in 7 adults. CKD results in a gradual loss of kidney function over time. Sometimes CKD doesn’t produce any significant symptoms until it reaches an advanced stage. On the other hand, acute kidney injury (AKI) accounts for a sudden decline in the kidney’s function. As a result, the kidneys fail to filter waste materials from the …

Multivariate Time Series Classification Of Sensor Data From An Industrial Drying Hopper: A Deep Learning Approach, Md Mushfiqur Rahman

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent years, the advancement of industry 4.0 and smart manufacturing has made a large number of industrial process data attainable with the use of sensors installed in the machineries. This thesis proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms in order to classify Multivariate Time Series (MTS) data into two categories- failure or unusual events and regular events, thus formulating the problem as binary …

Flipping The Classroom For Introduction To Probability And Statistics For Engineers, Philomena Krosmico

Graduate Theses, Dissertations, and Problem Reports (ETD)

Introduction to Probability and Statistics for Engineers, IENG 213, is a foundational course in the Industrial Engineering curriculum at West Virginia University (WVU). The challenge has been finding the best teaching method to instill concept learning. A “flipped classroom” teaching style has been gaining momentum throughout higher education and has had proven success in STEM fields. At WVU, beginning with the 2016 Fall semester, the teaching model for the class used a flipped classroom style for one of the instructors. Data on statistical concept learning, using the University of Oklahoma validated statistics concept inventory instrument (Allen K., 2006), was collected …

Sustainability Key Performance Indicators For Mass Customization, Md Fahid Hasan Pulak

Graduate Theses, Dissertations, and Problem Reports (ETD)

Today’s manufacturers are striving towards a more sustainable and customized product offering in their value chain to satisfy customer demand and compete on the marketplace. By adopting sustainability practices, companies are not only complying with environmental regulations but are strategically addressing the triple bottom line (TBL) of sustainability (environmental, social, and economic). Similarly, mass customization allows a company to better satisfy their customers by creating individualized products economically. Moving forward, it is important to better understand the relationship of these two competitive strategies. In order to assess the sustainability performance of mass customization, it is important to understand the appropriate …

Effect Of Feedrate, Depth Of Cut, Tool Material, And Toolpath On Dimensional Accuracy And Surface Roughness Of Milled Cfrp, Assem Hesham Almadani

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis investigates the effect of different factors on Carbon Fiber Reinforced Polymers (CFRP) milling, like feedrate, tool material, and cutting speed. CFRP offers excellent material properties, which led to the increase of the material in today's manufacturing industry. CFRP offers up to 2.25 times steel's modulus of elasticity at about a fifth of the weight and excellent thermal properties, which allow the use of this material in applications with high heat like automobiles. Many industries have implemented the use of CFRP in their applications, like airplanes and automobiles, which lead to a decrease in weight and increase in strength. …

An Equest Based Building Energy Modeling Analysis For Energy Efficiency Of Buildings, Saroj Lamichhane

Graduate Theses, Dissertations, and Problem Reports (ETD)

Building energy performance is a function of numerous building parameters. In this study, sensitivity analysis on twenty parameters is performed to determine the top three parameters which have the most significant impact on the energy performance of buildings. Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in eQUEST. The model is calibrated using Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method. The model satisfies the NMBE and CV(RMSE) criteria set by the American Society of Heating, Refrigeration, and Air-Conditioning (ASHRAE) Guideline 14, …

Prediction Of Tensile Behaviors Of L-Ded 316 Stainless Steel Parts Using Machine Learning, Israt Zarin Era

Graduate Theses, Dissertations, and Problem Reports (ETD)

Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed and specimen height, play a great deal in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, a data driven machine learning model XGBoost has been built and applied to predict the …

Determination Of Effectiveness Of Energy Management System In Buildings, Vivash Karki

Graduate Theses, Dissertations, and Problem Reports (ETD)

Building Energy Management Systems (BEMS) are computer-based systems that aid in managing, controlling, and monitoring the building technical services and energy consumption by equipment used in the building. The effectiveness of BEMS is dependent upon numerous factors, among which the operational characteristics of the building and the BEMS control parameters also play an essential role. This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS. The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help …

Identification Of Moving Bottlenecks In Production Systems, Funmilayo Mofoluwasola Adeyinka

Graduate Theses, Dissertations, and Problem Reports (ETD)

Manufacturing sector have been plagued by bottlenecks from time immemorial, leading to loss of productivity and profitability, various research effort has been expended towards identifying and mitigating the effects of bottlenecks on production lines. However, traditional approaches often fail in identifying moving bottlenecks. The current data boom and giant strides made in the machine learning field proffers an alternative means of using the large volume of data generated by machines in identifying bottlenecks. In this study, a hierarchical agglomerative clustering algorithm is used in identifying potential groups of bottlenecks within a serial production line.

A serial production line with five …

Models And Solution Approaches For Integrated Student To School Assignment And School Bus Routing Problem Focusing On Special Needs Students, Azadeh Ansari

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation addresses the integrated problem of assigning students to schools and generating school bus routes particularly focusing on the special needs students is addressed. Special needs students generally require supplementary accommodations and must be picked up from and dropped off at their home addresses. This will increase the number of nodes in the network and therefore introduces additional complexities to the problems of assignment and routing for students. An integrated single objective mathematical model is first developed that simultaneously assigns the students to schools based on their needs and generates efficient bus routes to deliver the students to their …