Operations Research, Systems Engineering and Industrial Engineering Commons^™

Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn

Graduate Theses and Dissertations

Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …

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 …

Nanoparticulate Carriers For Drug Delivery, Samantha Lokelani Crossen, Tarun Goswami

Biomedical, Industrial & Human Factors Engineering Faculty Publications

Drug delivery with nanoparticulate carriers is a new and upcoming research area that is making major changes within the pharmaceutical industry. Nanoparticulate carriers are discussed, particularly, engineered nanoparticulate carriers used as drug delivery systems for targeted delivery. Nanoparticulate carriers that are used for drug delivery systems include polymers, micelles, dendrimers, liposomes, ceramics, metals, and various forms of biological materials. The properties of these nanoparticulate carriers are very advantageous for targeted drug delivery and result in efficient drug accumulation at the targeted area of interest, reduced drug toxicity, reduced systemic side effects, and more efficient use of the drug overall. Nanoparticlulate …

Carbon Nanotori Reinforced Lubricants In Plastic Deformation Processes, Jaime Taha-Tijerina, Juan Manuel Martinez, Daniel Euresti, Patsy Yessenia Arquieta-Guillen

Manufacturing & Industrial Engineering Faculty Publications

This research presents the effects of carbon nanotori structures (CNst) dispersed as reinforcement for metal-working and metal-forming lubricants. Synthetic (SL) and deep drawing (DD) nanolubricants were prepared following a two-step method at 0.01 wt.%, 0.05 wt.%, and 0.10 wt.% filler fractions. Slight increases in viscosity (oz) increased by 16% and 22% at merely 0.01 wt.% CNst reinforcement and up to 73% and 107% at 0.10 wt.% filler fraction for SL and DD nanolubricants, respectively, compared to conventional materials. Additionally, at 0.10 wt.% wear scar evaluations showed a highest benefit of 16% and 24%, for SL and DD nanolubricants, respectively. This …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

An Elliptical Cover Problem In Drone Delivery Network Design And Its Solution Algorithms, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

Given n demand points in a geographic area, the elliptical cover problem is to determine the location of p depots (anywhere in the area) so as to minimize the maximum distance of an economical delivery trip in which a delivery vehicle starts from the nearest depot to a demand point, visits the demand point and then returns to the second nearest depot to that demand point. We show that this problem is NP-hard, and adapt Cooper’s alternating locate-allocate heuristic to find locally optimal solutions for both the point-coverage and area-coverage scenarios. Experiments show that most locally optimal solutions perform similarly …

Advancements Of Autonomous Applications, Jessica Massey, Jeremy Evert

Student Research

This material is based upon work supported by the National Aeronautics and Space Administration under Grant Agreement No. 80NSSC20M0114 issued through Oklahoma Space Grant Consortium. This research is in support of the Fire Dawgs competition team for this year’s SpeedFest competition at Oklahoma State University. This NASA OK Space Grant Consortium funded competition team will compete in the Charlie Class, where an autonomous vehicle will navigate a course and put out a fire.

Robots and self-driving vehicles are useful, especially for hazardous jobs, such as firefighting. The use of high-tech sensing technology is a small part of how self-driving vehicles …

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 …

21st Century Leadership: Leadership In Challenging Times, Adam Morris

Operations Management Presentations

Our global society has experienced unprecedented changes in the last couple years. The COVID-19 pandemic created drastic changes to our personal and professional lives. We went to a virtual work and learning environment almost overnight. People in leadership positions had to make impossible decisions on items no one has ever experienced. With all the changes that are happing in our world, what does it take to be a leader in today’s global society? Do basic leadership principles from 30 years ago still apply? Watch the latest installment of the MSEM/MSOM Lunch & Learn Webinar Series that will discuss these leadership …

Designing For The Future: Sensor And Gauge Assembly Work Cell Design, Kaitryana Leinbach

Honors Capstones

No abstract provided.

Simulation, Optimization, And Economic Assessment Of Pelamis Wave Energy Converter, Hana Ghaneei, Mohammadreza Mahmoudi

Faculty Articles, Papers, and Other Scholarship

Wave energy and power is accessible on almost any body of water. One of the most widely known floating structures to generate renewable energy from the seas and the ocean is wave energy converter Pelamis. In this study, an attempt was made to simulate the dynamic behaviour of Pelamis P2 in the software AQWA under the influence of a nonlinear second-order Stokes wave. Pelamis P2 was simulated in different marine conditions including different water depths, wave heights, periods, and angles to assess its optimal operation. With the results in mind, it can be argued that with an increase in water …

The Traveling Salesman Problem: An Analysis And Comparison Of Metaheuristics And Algorithms, Mason Helmick

Senior Honors Theses

One of the most investigated topics in operations research is the Traveling Salesman Problem (TSP) and the algorithms that can be used to solve it. Despite its relatively simple formulation, its computational difficulty keeps it and potential solution methods at the forefront of current research. This paper defines and analyzes numerous proposed solutions to the TSP in order to facilitate understanding of the problem. Additionally, the efficiencies of different heuristics are studied and compared to the aforementioned algorithms’ accuracy, as a quick algorithm is often formulated at the expense of an exact solution.

Statistical Monitoring The Quality Of Healthcare Services, Yanqing Kuang

USF Tampa Graduate Theses and Dissertations

In today’s healthcare industry, quality of care is a growing focus in the delivery of healthcare. To improve the quality of care in healthcare delivery, many studies focus on the longterm operational decision making to meet the expectations of healthcare providers and users, such as medical resource allocation, bed planning, staff scheduling, etc. These problems are typically parts of long-term operational decision making, however, time is essential in healthcare system. To ensure the adherence to a high quality of care and detect deterioration in real time, the quality of service should be measured over days or hours instead of just …

Evaluation Of Microstructural And Mechanical Behavior Of Ahss Cp780 Steel Welded By Gmaw-Pulsed And Gmaw-Pulsed-Brazing Processes, Alan Jadir Romero-Orozco, Jaime Taha-Tijerina, Rene De Luna-Alanis, Victor Hugo Lopez-Morelos, Maria Del Carmen Ramirez, Melchor Salazar-Martinez, Francisco Fernando Curiel-Lopez

Manufacturing & Industrial Engineering Faculty Publications

Joints of complex phase 780 (CP-780) advanced high strength steel (AHSS) were carried out by using an ER-CuAl-A2 filler metal for the gas metal arc welding pulsed brazing (GMAW-P- brazing) process and the ER-80S-D2 for the GMAW-P process employing two levels of heat input. The phases in the weld bead and HAZ were analyzed, and the evaporation of zinc by means of scanning electron microscopy (SEM) was also monitored. The mechanical properties of the welded joints were evaluated by tension, microhardness and vertical impact tests. It was found that there was greater surface Zn evaporation in the joints welded with …

Technical Inventory Management For College Of Liberal Arts, Sofya Shatalova

Industrial and Manufacturing Engineering

This project is sponsored by California Polytechnic State University’s Information Technology Services (ITS) department aimed to help with the transfer of inventory management from ITS to the College of Liberal Arts’ departments. Currently, there are no guidelines on how equipment is handled and little to no information about the flow of equipment. This creates difficulty for faculty and staff within those departments to check out, look up, or keep track of equipment. The sponsor needs for each department to be responsible for their own equipment and to familiarize themselves with the old database system the ITS used.

After understanding the …

Predicting Tf33-Pw-100a Engine Failures Due To Oil Issues Using Survival Analyses, Anna M. Davis

Theses and Dissertations

In 2007, the Office of the Assistant Secretary of Defense for Sustainment pushed for the need to transition to a Condition Based Maintenance Plus (CBM ) initiative for weapon systems in the U.S. Department of Defense. The CBM initiative can help increase aircraft availability (AA) for the United States Air Force. There are many reasons where AA can be affected but one such issue is engine availability primarily due to oil issues. Within the CBM perspective, this study examines the risk of a jet engine failure due to an oil issue and attempts to predict an engines time until next …

Analysis And Simulation Of Energy Performances Of Additive Manufacturing Systems, Mohammad Hassan

LSU Doctoral Dissertations

Additive manufacturing (AM) is an evolving technology that offers distinct advantages over conventional subtractive manufacturing by fabricating a variety of complex-shaped parts with mass customization. Selective laser melting (SLM), electron beam melting (EBM), and fused filament fabrication (FFF) are popular AM processes. A recent growing trend in AM suggests the necessity of an energy study to explore energy performance measures. In this research, a comprehensive energy study is performed for various AM processes with three objectives. The first objective of this study is to assess relationships between energy performance measures and two key process parameters (infill patterns and infill percentages) …

State Of Industry 5.0—Analysis And Identification Of Current Research Trends, Aditya Akundi, Daniel Euresti, Sergio Luna, Wilma Ankobiah, Amit Lopes, Immanuel Edinbarough

Manufacturing & Industrial Engineering Faculty Publications

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing …

Tools & Visualization Techniques Available To Support The Operations Manager, Kirk Michealson

Operations Management Presentations

Most organizations provide their employees the Microsoft Office Tools Suite with Excel. The University of Arkansas Master of Science in Operations Management (MSOM) Program has included Excel as a foundational aspect of the program for the last 7 years. In that time, Microsoft has increased the Excel capabilities dramatically. Simultaneously, business intelligence analytics interest has increased, and Tableau has been created as a visual analytics platform to help users see and understand data for modern business intelligence. As a result, to support the Operations Manager the MSOM Program has developed a new course to present the additional tools and visualization …

Biomechanical Evaluation Of Recurrent Dissociation Of Modular Humeral Prostheses, Daniel B. Luckenbill, Mike F. Iossi, Alyssa M. George Whitney, Danielle Miller, Lynn A. Crosby, Tarun Goswami

Biomedical, Industrial & Human Factors Engineering Faculty Publications

The purpose of the study was to evaluate the force and torque required to dissociate a humeral head from the unimplanted modular total shoulder replacement system from different manufacturers and to determine if load and torque to dissociation are reduced in the presence of bodily fluids. Impingement, taper contamination, lack of compressive forces, and interference of taper fixation by the proximal humerus have all been reported as possible causes for dissociation. Experimental values determined in this research were compared with literature estimates of dissociation force of the humeral head under various conditions to gain more understanding of the causes of …

Molecular Dynamic Simulation Of Diffusion In The Melt Pool In Laser Additive Alloying Process Of Co-Ni-Cr-Mn-Fe High Entropy Alloy, Mathew Farias, Han Hu, Shanshan Zhang, Jianzhi Li, Ben Xu

Manufacturing & Industrial Engineering Faculty Publications

High entropy alloys (HEAs) can be manufactured in many conventional ways, but it becomes difficult of fabricating heterogeneous materials and structures. Selective Laser Melting (SLM) method generally melts pure elemental powders or prefabricated alloy powders without alloying process. In-situ alloying in SLM, which is also called Laser Additive Alloying (LAA), using pure elemental powders becomes a promising method for creating HEA with heterogeneous structures. However, the effect of the diffusion of elements in the molten pool on the formation of HEA remains unclear. In this paper, the well-discussed Cantor HEA was studied in an in-situ alloying situation, where pure elemental …

Inpatient Discharge-By-Noon: Are Fewer Better Than All?, Nicholas Ballester, Pratik J. Parikh, Kara Combs, Jordan S. Peck

Journal of Maine Medical Center

Introduction: To address boarding in hospital emergency departments, discharge-by-noon could free up inpatient beds earlier in the day. However, discharging all patients by noon can heavily burden inpatient units and may not be feasible. In this study, we determine the number of discharges after which the benefits of an additional discharge-by-noon diminish.

Methods: We conducted a simulation analysis to quantify how occupancy rate, mean daily number of discharges, and peak discharge time impact upstream boarding time in an inpatient neurology unit at Maine Medical Center. Using a day-of-discharge simulation model with one year of retrospective data, we assessed configurations approximating …

Data-Driven Semantic Modeling For Welding Assemblies, Fahim Ahmed

Wayne State University Dissertations

A significant information gap is prevalent between the design domain and the manufacturing domain. The designers lack manufacturing awareness as they have little knowledge regarding the manufacturability of their designs. The welding domain falls prey to this issue as designers lack manufacturing awareness and welding engineers lack weldability of the product assembly. Data-driven techniques have shown promising results in the analysis and understanding of complex welding processes. Data analytics play a significant role to turn data into valuable insights to assist in the weldability certification decision-making or weldability prediction for Resistance Spot Welding (RSW) as well. We have used machine …

Optimization-Based Uav Fleet Routing And Safety Assurance – Models, Algorithms, And Prototyping, Zhenyu Zhou

Wayne State University Dissertations

Unmanned aerial vehicles (UAVs), especially multi-rotor drones, have been increasingly used in various scenarios in the last decade. With the reduced hardware costs, improved battery life, and enhanced processor performance, we can eventually allow all kinds of drones to automatically travel through the low-altitude airspace. The large-scale application of drones will extend the basic transportation facilities from the ground to the air and form 3D transportation networks for the future. Compared to current ground-vehicle and aircraft traffic systems, multi-UAV systems are far from well-developed. Most current multi-UAV systems are human-operated or pre-programmed to perform specific tasks. The current application of …

Automotive Product Assortment Planning With Consideration Of Distribution Channel Dynamics, Madagedara Maduka Rushanjalee Balasooriya

Wayne State University Dissertations

Automotive original equipment manufacturers (OEMs) are putting a lot of effort into maintaining an efficient order catalog to offer better products to their customers in maketo-stock (MTS) markets. While product “assortment planning" research grows to more effectively identify the best assortments for OEMs, the existing configurable assortment planning literature ignores a significant dimension: the impact of distribution channels, especially dealer franchise networks. Dealers face unique challenges in trying to best satisfy the choice preferences of their local consumers by balancing their limited product configuration inventory with profitability. In many predominantly MTS automotive markets such as the U.S., the reality is …

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 …

Acute Coronary Syndrome Prediction: A Data-Driven Machine Learning Modeling Approach In Emergency Care, Joshua Oluwatobiloba Emakhu

Wayne State University Dissertations

Healthcare facilities are faced with significant challenges all year round, with patients presenting to the emergency department (ED) with different health issues. Of these challenges, heart disease seems to be an outlier. With heart disease being the primary cause of mortality and morbidity in both developed and developing countries, clinical concerns for acute coronary syndrome (ACS) are one of emergency medicine’s most common patient encounters. Of the three sub-categories of ACS, non-ST-segment elevation myocardial infarction (NSTEMI) has a long-term impact on the well-being of patients if left untreated. Previous efforts in hospital management have applied machine learning algorithms in differentiating …

Learning And Decision Making In Social Media Networks, Zhecheng Qiang

Electronic Theses and Dissertations, 2020-2023

Social media is a virtual community where users share news, ideas, interests, and information. Learning the information diffusion dynamics and making decisions correspondingly, e.g., selecting the seed nodes to maximize the influence, have been widely applied to the areas of viral marketing and cyber security. In this dissertation, we study the problem of learning diffusion process, i.e., infection prediction, in social media networks utilizing both feature-based machine learning methods and mathematical model-based methods. For feature-based machine learning methods, the neighborhood information is treated as an important feature together with user profile and content similarity features. For model-based methods, two distinctive …

Decoding Task-Based Fmri Data Using Graph Neural Networks, Considering Individual Differences, Maham Saeidi

Electronic Theses and Dissertations, 2020-2023

Functional magnetic resonance imaging (fMRI) is a non-invasive technology that provides high spatial resolution in determining the human brain's responses and measures regional brain activity through metabolic changes in blood oxygen consumption associated with neural activity. Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific task performance. Over the past several years, a variety of computational methods have been proposed to decode task fMRI data that can identify brain regions associated with different task stimulations. Despite the advances made by these methods, several limitations exist due to graph representations and graph embeddings transferred …

Complex Quantum Contagion: A Quantum-Like Approach For The Analysis Of Co-Evolutionary Dynamics Of Social Contagion, Ece Mutlu

Electronic Theses and Dissertations, 2020-2023

Modeling the dynamics of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information (or behavior), there are no studies that take into account the individuals' uncertainty during their decision-making despite its theoretical and experimental evidence in behavioral economics, decision science, cognitive science, or multi-agent systems. This …