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Articles 1 - 10 of 10
Full-Text Articles in Engineering
Reliability Modeling And Improvement Of Critical Infrastructures: Theory, Simulation, And Computational Methods, José Carlos Hernández Azucena
Reliability Modeling And Improvement Of Critical Infrastructures: Theory, Simulation, And Computational Methods, José Carlos Hernández Azucena
Graduate Theses and Dissertations
This dissertation presents a framework for developing data-driven tools to model and improve the performance of Interconnected Critical Infrastructures (ICIs) in multiple contexts. The importance of ICIs for daily human activities and the large volumes of data in continuous generation in modern industries grant relevance to research efforts in this direction. Chapter 2 focuses on the impact of disruptions in Multimodal Transportation Networks, which I explored from an application perspective. The outlined research directions propose exploring the combination of simulation for decision-making with data-driven optimization paradigms to create tools that may provide stakeholders with optimal policies for a wide array …
Characterizing Logistics Operations Within A Federal Staging Area For Hurricane Response: A Qualitative Analysis Of Federal, State And Local Perspectives, Jannatul Shefa
Graduate Theses and Dissertations
A successful deployment of logistics operations following a disaster is a collective contribution of federal, state, and local entities to ascertain an efficient and effective response. This research analyzes data from interviews with disaster response logistics experts from these entities. The objective is to investigate the information sources and planning processes used in these organizations to plan vehicle routes for critical resource deliveries to impacted areas. Special attention is directed to the impacts of incomplete knowledge of infrastructure status, such as road disruptions due to debris or flooding. Supported by both qualitative and quantitative evidence, the study finds that incomplete …
Demand Prediction And Inventory Management Of Surgical Supplies, Rajon Paul Pantha
Demand Prediction And Inventory Management Of Surgical Supplies, Rajon Paul Pantha
Graduate Theses and Dissertations
Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for …
Automation Of Life Cycle Assessment, Jacob Hickman
Automation Of Life Cycle Assessment, Jacob Hickman
Graduate Theses and Dissertations
An automation program, named Jacob LCA, was created to help perform life cycle assessment (LCA). The program uses a template file to perform controlled and consistently ordered actions with the LCA program, SimaPro, and effectively removes the need for manual user input. It can be set to run using data from one or more life cycle inventory (LCI) files, which allows for rapid iteration and testing of data. It also partially addresses some of the limitations of LCA by establishing a procedure through which temporal and spatial variations in data can be integrated into LCI files and then passed to …
Efficient Routing For Disaster Scenarios In Uncertain Networks: A Computational Study Of Adaptive Algorithms For The Stochastic Canadian Traveler Problem With Multiple Agents And Destinations, Neel Chanchad
Graduate Theses and Dissertations
The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as …
The Impact Of A Carbon Tax On Emissions, Jessica Creech
The Impact Of A Carbon Tax On Emissions, Jessica Creech
Industrial Engineering Undergraduate Honors Theses
A carbon tax is an economic policy that aims to reduce various emissions to serve the protection of the environment. Versions of this policy have been implemented in multiple countries across the world to introduce a cost for contributing to environmental damage. Since climate change is prevalent in today’s world, finding an effective method to reduce emissions is essential. However, many countries hesitate to utilize a carbon tax for two reasons. First, they are unsure if the carbon tax is effective at reducing emissions. Second, there is a concern that the implementation of such a tax will be detrimental to …
Testing The Effects Of Different Designs On The Physical Properties Of 3d-Printed Watch Bands, Ross Harper
Testing The Effects Of Different Designs On The Physical Properties Of 3d-Printed Watch Bands, Ross Harper
Industrial Engineering Undergraduate Honors Theses
Technological innovation progresses at an ever-increasing rate, and this is especially true in the field of 3D-printing. 3D-printing has become popular in manufacturing settings and among amateur hobbyists alike, largely because 3D-printers can fabricate an enormous number of designs from an array of materials and allow for fine-tuning through several setting options. Individuals with proficient 3D-printing abilities can produce a nearly infinite number of components for diverse applications in manufacturing, recreation, ergonomics, and many more. Some individuals use their skills to create functional substitutes for name-brand items, including bands to fit and be worn with a smart watch. However, little …
Detecting Pathobiomes Using Machine Learning, Valerie Jackson, Valerie Jackson
Detecting Pathobiomes Using Machine Learning, Valerie Jackson, Valerie Jackson
Industrial Engineering Undergraduate Honors Theses
Machine learning is a field with high growth potential due to the overall continuous progressions, developments, advancements, and improvements caused by the way it is used to help interpret and use large amounts of data [1]. One type of data that can be collected and analyzed by these machine learning models is data that is associated with DNA and information that the DNA gives. The research will be focusing specifically on using machine learning technology to detect pathobiomes indicative of salmonella pork. The pathobiome associated with salmonella is very similar to others, and this causes a problem for classification/detection with …
Automated Visualization Pipeline For Near Real-Time Risk Management System, Paris Joslin
Automated Visualization Pipeline For Near Real-Time Risk Management System, Paris Joslin
Industrial Engineering Undergraduate Honors Theses
In modern society, technological capabilities and the amount of data readily available to users continue to grow exponentially. Many have adopted these new capabilities but lack the infrastructure needed to efficiently utilize high-powered software and programs. Without a method to collect, store, and process large datasets in real-time, individuals and businesses can quickly become overwhelmed, inhibiting effective decision-making processes. There is potential to improve decision-making abilities by enhancing the computing infrastructure. To accomplish this task, we will explore the ideas surrounding High Performance Computing (HPC) and data visualization software. High Performance Computing is the ability to process data and perform …
Interaction Effects And Selecting Regression Models Of Taylor Swift Song Popularity, Halle Schneidewind
Interaction Effects And Selecting Regression Models Of Taylor Swift Song Popularity, Halle Schneidewind
Industrial Engineering Undergraduate Honors Theses
Understanding music popularity and what drives it is important not only for artists but for other individuals who are financially tied to music sales including producers, writers, and record labels. Studies have been done to define how a song’s popularity can be measured, what attributes or features are drivers for popularity, and to what extent can a song’s popularity even be predicted. This paper takes two linear regression approaches to predicting the popularity of a Taylor Swift song on Spotify based on auditory features the Spotify API estimates and historic popularity of songs on Spotify. One model takes into consideration …