Operational Research Commons^™

Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of ...

The Intersection Of Robotic Process Automation And Lean Six Sigma Applied To Unstructured Data, Emily Mcintosh

Operations Research and Engineering Management Theses and Dissertations

While new Artificial Intelligence (AI) technologies gain traction in the workplace, there seems to be more buzz around these newer advances, including Robotic Process Automation (RPA), than more established process improvement techniques such as Lean Six Sigma. This praxis research uses Lean Six Sigma as a framework for effectively deploying these emerging technologies, a challenge for 86% of companies (Ernst & Young, 2021). This research is applied to one of the legal industry’s most resource intensive processes – eDiscovery in the environment of a Big 4 accounting firm that provides services to corporations and legal professionals alike.

Electronic discovery (also known ...

Comparing Actively Managed Mutual Fund Categories To Index Funds Using Linear Regression Forecasting And Portfolio Optimization, Luke Weiner

Industrial Engineering Undergraduate Honors Theses

The global investment industry offers a wide variety of investment products especially for individual investors. One such product, index funds, which are younger than actively managed mutual funds, have typically outperformed managed funds. Despite this phenomenon, investors have displayed a tendency to continue investing in actively managed funds. Although only a small percentage of actively managed funds outperform index funds, the costs of actively managed funds are significantly higher. Also, managed fund performances are most often determined by their fund category such as growth or real estate. I wanted to answer the following question for individual investors: can we forecast ...

Analyzing Vulnerabilities In The Northwest Arkansas Highway Network Using Mathematical Optimization, Brandon Jerome

Industrial Engineering Undergraduate Honors Theses

The highway and bridge network is a critical infrastructure that allows for the free transportation of citizens and enables truck-borne freight transportation. Disruption of this system could be caused by a terrorist attack, natural disaster, growth of population, required repairs and upgrades, or collapse caused by old age or malfunction. In the event of a disruption cities and regions can experience increased traffic and supply chain shortages, thus causing cascading effects throughout surrounding areas. With this motivation, we develop a network interdiction optimization model to identify a limited subset of roads that, if disrupted, causes the greatest increase in the ...

A Study Of Scheduling Problems With Sequence Dependent Restrictions And Preferences, Nitin Srinath

All Dissertations

In some applications like fabric dying, semiconductor wafer processing, and flexible manufacturing, the machines being used to process jobs must be set up and serviced frequently. These setup processes and associated setup times between jobs often depend on the jobs and the sequence in which jobs are placed onto machines. That is, the scheduling of jobs on machines must account for the sequence-dependent setup times as well. These setup times can be a major factor in operational costs. In fabric dyeing processes, the sequence in which jobs are processed is also important for quality, i.e., there is a strong ...

Optimization Methods For Day Ahead Unit Commitment, Jonathan David Schrock

Doctoral Dissertations

This work examines a variety of optimization techniques to better solve the day ahead unit commitment problem. The first method looks at the impact of almost identical generators on the problem and how to exploit that fact for computational gain. The second work seeks to improve the fidelity of the problem by better modeling the impact of pumped storage hydropower. Lastly, the relationship between the length of the planning horizon and the quality of the solutions is investigated.

Selected Interdiction Games With Uncertain, Risk-Averse, And Simultaneous Play Considerations, Di H. Nguyen

All Dissertations

This dissertation examines two network interdiction problems: a shortest-path interdiction problem under uncertainty and a network interdiction problem in a simultaneous game. Both problems happen in two stages over a directed network, and involve a leader and a follower who have opposing interests.

In the first problem, the leader acts first to lengthen a subset of arcs, and a follower acts second to select a shortest path across the network. The cost for a follower’s arc consists of a base cost if the arc is not interdicted, plus an additional cost that is incurred if the arc is interdicted ...

Design And Analysis Of Efficient Freight Transportation Networks In A Collaborative Logistics Environment, Vishal Badyal

All Dissertations

The increase in total freight volumes, reducing volume per freight unit, and delivery deadlines have increased the burden on freight transportation systems of today. With the evolution of freight demand trends, there also needs to be an evolution in the freight distribution processes. Today's freight transportation processes have a lot of inefficiencies that could be streamlined, thus preventing concerns like increased operational costs, road congestion, and environmental degradation. Collaborative logistics is one of the approaches where supply chain partners collaborate horizontally or/and vertically to create a centralized network that is more efficient and serves towards a common goal ...

Finding Core Members Of A Hedonic Game, Daniele M. Vernon-Bido

Computational Modeling & Simulation Engineering Theses & Dissertations

Agent-based modeling (ABM) is a frequently used paradigm for social simulation; however, there is little evidence of its use in strategic coalition formations. There are few models that explore coalition formation and even fewer that validate their results against an expected outcome. Cooperative game theory is often used to study strategic coalition formation but solving games involving a significant number of agents is computationally intractable. However, there is a natural linkage between ABM and the study of strategic coalition formation. A foundational feature of ABM is the interaction of agents and their environment. Coalition formation is primarily the result of ...

Complex System Governance Leadership, David C. Walters

Engineering Management & Systems Engineering Theses & Dissertations

The purpose of this research was to develop a systems theory-based framework for leadership in governance of complex systems. Recognizing complexity and uncertainty as norms for the environments in which organizations exist encouraged researchers to suggest complexity theory, complex systems, and complex adaptive systems as appropriate for addressing these conditions. Complex System Governance (CSG), based in systems theory, management cybernetics, and governance, endeavors to provide for the design, execution and evolution of functions that provide control, communication, coordination, and integration at the metasystem level to support operations and continued system existence (viability). From a management cybernetics perspective, CSG leadership has ...

Application Of A Polynomial Affine Method In Dynamic Portfolio Choice, Yichen Zhu

Electronic Thesis and Dissertation Repository

This thesis develops numerical approaches to attain optimal multi-period portfolio strategies in the context of advanced stochastic models within expected utility and mean-variance theories. Unlike common buy-and-hold portfolio strategies, dynamic asset allocation reflects the investment philosophy of a portfolio manager that benefits from the most recent market conditions to rebalance the portfolio accordingly. This enables managers to capture fleeting opportunities in the markets thereby enhancing the portfolio performance. However, the solvability of the dynamic asset allocation problem is often non-analytical, especially when considering a high-dimensional portfolio with advanced models mimicking practical asset's return. To overcome this issue, this thesis ...

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.

Decision-Analytic Models Using Reinforcement Learning To Inform Dynamic Sequential Decisions In Public Policy, Seyedeh Nazanin Khatami

Doctoral Dissertations

We developed decision-analytic models specifically suited for long-term sequential decision-making in the context of large-scale dynamic stochastic systems, focusing on public policy investment decisions. We found that while machine learning and artificial intelligence algorithms provide the most suitable frameworks for such analyses, multiple challenges arise in its successful adaptation. We address three specific challenges in two public sectors, public health and climate policy, through the following three essays.

In Essay I, we developed a reinforcement learning (RL) model to identify optimal sequence of testing and retention-in-care interventions to inform the national strategic plan “Ending the HIV Epidemic in the US ...

Inverse Optimization: Inferring Unknown Instance Parameters From Observed Decisions, Keith Batista

Theses and Dissertations

The objective of this research is to develop procedures that estimate selected, unknown parameters over an adversary's investment portfolio across a set of new or existing technologies. To solve for the selected unknown parameters, it is assumed that the adversary is maximizing the portfolio optimization problem and investing along the efficient frontier. The first technique is when an unknown risk attitude exists but all other parameters were known (i.e. expected return, variance, covariance). An adaptive line search technique that iteratively solved the portfolio optimization problem until the adversary's risk parameter was found. The second problem that was ...

A Decision Support Simulation To Analyze Scheduling Alternatives For Applicant Processing At Military Entrance Processing Stations (Meps), Jonathan M. Escamilla

Theses and Dissertations

Applicant processing at Military Entrance Processing Stations (MEPS) is conducted via a batch arrival process by which all applicants arrive at the beginning of the processing day. Pursuit of alternate processing scenarios has never progressed beyond the pilot stage, possibly because the Command lacks a general decision support model to evaluate the impacts of proposed policies on applicant processing operations. This research creates a discrete event simulation of MEPS applicant processing operations and applies the model to three alternative applicant processing scenarios: split-shift, appointment- based, and express-lane. Results are examined and compared to benchmarks using multiple performance measurements.

Comparison Of Lightning Warning Radii Distributions, Michael M. Maestas

Theses and Dissertations

Previous research investigating lightning warning radii about the Cape Canaveral space launch facilities have focused on reducing these radii from either 5 nautical miles (NM) to 4 NM or from 6 NM to 5 NM depending on the structures being protected. Some of these findings have suggested the possibility of both a seasonal difference (warm versus cold) and lightning detection events (cloud-to-ground lightning (CG) or total lightning (TL)) impacting these radii and associated risk levels. Utilizing the 2017-2020 data provided by the 45th Weather Squadron at Patrick Space Force Base via the Mesoscale Eastern Range Lightning Information System (MERLIN), this ...

Training Logic And Random Forest Models To Predict It Spending, Jacob P. Batt

Theses and Dissertations

The Air Force must modernize, but the distribution of funds for technology remains as tight as ever. To this end, the Air Force Audit Agency is looking to utilize machine learning techniques to enhance their capabilities. This research explores Logistic Regression and Random Forest modeling to streamline data collection and cost classification. The final Logistic Regression model identified 4 significant attributes out of the 36 given and was 85 accurate in predicting whether a purchase amount was over or under $10,000. To expand beyond binary classification, a six-category classification Random Forest model was developed. It identified 6 significant attributes ...

Narrative Analysis Of Open-Source Social Media Activity In The Indopacom Aor, Aaron K. Glenn

Theses and Dissertations

Emotion classification can be a powerful tool to derive narratives from social media data. Recurrent Neural Networks (RNN) can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that RNN variants can produce more than an 8% gain in accuracy in comparison to Logistic Regression and SVM techniques and a 15% gain over Random Forest when using FastText embeddings. This research found a statistical significance in the performance of a single layer Bi-directional Long Short-Term Memory (Bi-LSTM) model over a 2-layer stacked Bi-LSTM model. This research also found ...

Optimal Aircraft Maneuvering Models For Cruise Missile Engagement: A Modeling And Computational Study, Izaiah G. Laduke

Theses and Dissertations

Given the increased threat and proliferation of adversary military capabilities, this research seeks to develop reasonably accurate and computationally tractable models to optimally maneuver aircraft to intercept cruise missile attacks. The research leveraged mathematical programming to model the problem, informed by constraints representing a system of (temporal) difference equations. The research began by comparing six models having alternative representations of velocity and acceleration constraints while analyzing situations with stationary targets. The Multiple Aircraft, Multiple Stationary Target Engagement Problem with Box Constraint Bounds (MAMSTEP-BC) Model yielded superior overall performance and was further analyzed through alternative mathematical programming model enhancements to create ...

Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell

Theses and Dissertations

The United States Air Force is investing in artificial intelligence (AI) to speed analysis in efforts to modernize the use of autonomous unmanned combat aerial vehicles (AUCAVs) in strike coordination and reconnaissance (SCAR) missions. This research examines an AUCAVs ability to execute target strikes and provide reconnaissance in a SCAR mission. An orienteering problem is formulated as anMarkov decision process (MDP) model wherein a single AUCAV must optimize its target route to aid in eliminating time-sensitive targets and collect imagery of requested named areas of interest while evading surface-to-air missile (SAM) battery threats imposed as obstacles. The AUCAV adjusts its ...