Operational Research Commons^™

Evaluating Climate Sentiment In Sec 10-K Filings: S&P 50 Companies, Ruby Chu

Dissertations, Theses, and Capstone Projects

Evaluating Climate Sentiment in SEC 10-K Filings: S&P 50 Companies investigates how climate sentiment is portrayed in corporate financial reporting, focusing on SEC 10-K filings from leading S&P 50 companies. These filings offer detailed insights into financial performance, risks, and management discussions, providing a rich dataset for analyzing corporate behaviors with sustainability and environmental concerns. This study aims to shed light on the extent to which companies address environmental issues and the implications for environmental stewardship by analyzing how these topics are portrayed in their SEC 10-K filings. Drawing inspiration from established greenwashing indicator frameworks, the study develops a climate …

Connection-Saving Gate Assignment: A Computational Approach, Rob Mailley

Computer Science Senior Theses

The growth of the commercial aviation industry has yielded many interesting problems in the field of Operations Research, many of which are now able to be solved as both technology and mathematical optimization improve. A particularly interesting problem in airport operations re- search is the Aircraft Gate Assignment Problem (AGAP), which seeks to create a feasible match- ing between planes and flights at an airport. This problem is well-suited to modeling with Integer Programming, and has attracted research since the 1970s. Researchers of the AGAP have considered many different objectives, ranging from airline-focused objectives to more passenger-focused objective functions. In …

Design Of Small-Scale Milk Processing Facility, Luke Bond, Robert Stenzel, Ry Steffen, John Van Nieuwenhuyse, Rossana Villa-Rojas, Terry Howell Jr., Tami M. Brown-Brandl, Forrest Kievit

Department of Agricultural and Biological Systems Engineering: Masters Project Reports

This project was intended to investigate the feasibility of a proposed modular dairy processing facility collocated with a small dairy on the University of Nebraska–Lincoln’s Innovation Campus. Priority was placed upon cost savings for equipment to be used in processing. Process flow diagrams, floor plans, production schedules, risk assessments, and plans for wastewater treatment were all developed to address the needs of the plant should it be built. A Monte Carlo simulation was conducted to evaluate the timeline for breaking even financially, resulting in the potential for a payback period of 4.5 years. The 4.5-year payback period was based on …

Reduction Of The Length Of Stay At The Emergency Department Of The Audie L. Murphy Hospital, Arwa Al Shikrian

Theses

We implemented the DMAIC framework (Define, Measure, Analyze, Improve, and Control) and Lean Six Sigma methods to reduce the length of stay (LOS) in the Emergency Department (ED) at Audie L. Murphy VA Hospital, a crucial issue affecting operational efficiency and patient care quality. This project was conducted in conjunction with an internship at the Hospital; our efforts were an integral part of a Green Belt project being conducted by Mr. Roarke Verkaik-Bushby, Hospital Administration Service, to

whom this author reported.

We employed quantitative research design, meticulously observing, measuring, and analyzing ED processes. We tracked patient flow, identified bottlenecks, and …

Open-Source Optimization For Green Last Mile Delivery And Other Applications, John Sooter

Industrial Engineering Undergraduate Honors Theses

Solving combinatorial optimization problems at scale and of sufficiently interesting context has historically required commercial solvers and access to proprietary company data. The development of performant open-source mathematical programming software and crowdsourced datasets has created an opportunity for individuals and enterprises alike to consider alternative solutions to problems with social and personal implications. This honors thesis represents a summary of my undergraduate research work, an application of optimization to three distinct problems connected to these developments. First, we present an optimization study of a last mile delivery system that shows optimization for energy consumption can generate vehicleindependent fuel savings at …

Modeling Of Multi-Period Disaster Logistics Planning In The State Of South Carolina, Emma Simon

All Theses

South Carolina is one of the most vulnerable states in the United States to the impact of hurricanes. Currently, when threatened with a natural disaster such as a hurricane, the state government makes many vital decisions based on knowledge and experience. In this study, the distribution of disaster relief commodities to meet immediate needs is analyzed through two models for the case of South Carolina to generate an optimal logistics strategy that considers the social vulnerability of affected populations. The first model is a multi-objective pre-disaster logistics model that uses a four-index formulation for the multiple trip vehicle routing problem. …

The Importance Of Data Preparation In A Data Science Problem, Sophia Beard

Data Science Undergraduate Honors Theses

This study is going to be based on an inventory outlier automation data science problem that is being solved to identify and prescribe inventory level outliers to help keep shelves stocked in terms of beverages. The objective of this paper will address why it is so important to understand the data that is involved in a particular data science problem and how planning ahead ensures a successful outcome in the data science world. In this data science project, Spatiotemporal Outlier Analysis for Inventory Intervention Automation, it was crucial for the team to understand, research, and visualize the data we were …

The Importance Of Text Representation For Neural Networks Through Natural Language Processing Techniques, William Parsley

Data Science Undergraduate Honors Theses

Text representation is a fundamental aspect of natural language processing (NLP) when it comes to the performance of neural networks. Free-form text fields are being utilized in more and more industries. Anything from a description of an item on a web store to tracking service events to military-grade aircraft is being collected in free-form text. The goal of the thesis is to highlight best practices and discuss trends in data to prepare text for a neural network. It will demonstrate various techniques for representing free-form text in the context of neural networks, focusing on data preparation decisions, embedding techniques, and …

Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati

Data Science Undergraduate Honors Theses

In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …

Examining Award Compliance To Inform Resource Allocation, Jacob Haarala

Data Science Undergraduate Honors Theses

This project focuses on JB Hunt Transport Inc's intermodal business unit (JBI) by focusing on the challenges associated with Published Pricing and Contractual Pricing. The primary issue revolves around the variance between the awarded freight volumes in Requests for Pricing (RFPs) and the actual volumes realized when the freight is shipped. This discrepancy poses challenges for effective sales planning, revenue goals, and optimal freight network management within JBI. Reporting tools, such as PowerBI, are currently used by JBI to provide insights into award compliance on a weekly basis. However, our goal with this project was to provide a deeper understanding …

Cost-Risk Analysis Of The Ercot Region Using Modern Portfolio Theory, Megan Sickinger

Master's Theses

In this work, we study the use of modern portfolio theory in a cost-risk analysis of the Electric Reliability Council of Texas (ERCOT). Based upon the risk-return concepts of modern portfolio theory, we develop an n-asset minimization problem to create a risk-cost frontier of portfolios of technologies within the ERCOT electricity region. The levelized cost of electricity for each technology in the region is a step in evaluating the expected cost of the portfolio, and the historical data of cost factors estimate the variance of cost for each technology. In addition, there are several constraints in our minimization problem to …

Denoising Diffusion Probabilistic Models Based Accelerated Mri, Alexander Francis Bugielski

Theses and Dissertations

Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability …

Adopting Information System Technologies In Construction Project Management, Hadi Haikal

Harrisburg University Dissertations and Theses

The construction sector has remained late in integrated information systems' adopting process to perform the system improving processes. Lack of adoption of technology has been a root cause of the early issues they faced, such as budgetary and scheduling overshoots, quality defects, safety occurrences, and generally poor communication and coordination among the stakeholders. This research looks into the benefits and challenges, the key success factors, and the implications of using an array of information systems, including building information modeling (BIM), artificial intelligence (AI), Internet of Things (IoT) sensing, and enterprise systems, to comprehensively address issues in project management in construction …

Analyzing The Impact Of Socioeconomic Factors On Cancer Clinical Trials Accessibility In The U.S. Using Machine Learning, Krysta L. Ray, Hiromi Honda

ATU Scholars Symposium

While cancer impacts all segments of the United States population, specific groups experience a disproportionate burden of the disease due to social, environmental, and economic disadvantages. This research examines the correlation between socioeconomic factors and the accessibility of cancer clinical trials across U.S. counties, employing a comprehensive dataset, County-Level Socioeconomic and Cancer Clinical Trial Data from Noah Ripper, and advanced machine-learning techniques. Our findings, derived from regression analysis and machine learning models like gradient boosting, highlight significant disparities in trial availability linked to socioeconomic indicators, including poverty rates, population estimates, median income, incidence rates, and mortality rates. Many regression models …

Optimization Of Human Interactions In The College Campus Model Via Simio Integration, Benjamin E. Chaback

Doctoral Dissertations and Master's Theses

College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, …

Understanding The Impact Of Emergent Conflict On Communication And Team Cognition: A Multilevel Study In Engineering Teams, Francisco Cima

Engineering Management & Systems Engineering Theses & Dissertations

The development of team cognition is crucial for fostering high-performing teams. In cognitive-intensive fields like engineering, effective communication serves as a primary precursor to team knowledge development, enabling group members to effectively retrieve and utilize each other's expertise. Despite the critical role of communication, there is a lack of empirical research examining how conflict situations, which are critical emerging factors inherent to teamwork, interact with communication processes to constrain team knowledge development and utilization. This study, rooted in information processing theory, investigates how emerging conflict shapes multilevel team knowledge structures by interacting with communication processes in engineering project teams. Prior …

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …

The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang

Theses and Dissertations

Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …

Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton

Theses and Dissertations

This research provides insights into a mixed integer linear programming model that finds the ideal number and type of Electric Vehicle Support Equipment (EVSE) required to meet U.S. military installations’ electric energy demands. Executive Order No. 14057 (2021) requires federal agencies to transition to electric non-tactical vehicles by 2035. This research determines minimum cost solutions to implement the transition incorporating real-world constraints, including the weekly vehicle mileage demand, EVSE cost, charging time, and EVSE capacity. This study contributes to the broader effort of the U.S. military to combat climate change and enhances the understanding of efficient EVSE deployment strategies in …

Techniques For Addressing Extreme Class Imbalance For Artificial Neural Networks Training, Colin W. Foley

Theses and Dissertations

This research examined the class imbalance problem while training convolutional neural networks (CNN) by applying different techniques to combat this common issue. This research used a modified CIFAR-10 dataset along with a curated aerial image dataset. Methods covered included undersampling, oversampling, synthetic minority oversampling technique, Edited Nearest Neighbors and combinations of the aforementioned methods. This research found that undersampling methods tended to outperform oversampling methods. While undersampling methods showed a decrease in overall accuracy, the increase in minority class prediction performance was promising enough to warrant further investigation.