Systematic Review Of Supply Chain Control Tower Critical Success Factors And Resilience Effects,
2024
Air Force Institute of Technology
Systematic Review Of Supply Chain Control Tower Critical Success Factors And Resilience Effects, Clay H. Chaffin
Theses and Dissertations
Supply chain control towers (SCCTs) are emerging as a vital component of modern supply chain management (SCM); however, research on SCCTs is limited and disjointed. This paper aims to uncover the critical success factors (CSFs) necessary for high-performing SCCTs, their relationship to the enablers and phases of supply chain resilience (SCRES), and the underlying theoretical framework of this relationship.
2033 Digital Modernization At Usmepcom: A Strategic Analysis Of Future Military Applicant Processing,
2024
Air Force Institute of Technology
2033 Digital Modernization At Usmepcom: A Strategic Analysis Of Future Military Applicant Processing, William A. Clay
Theses and Dissertations
This research examines the projected 2033 applicant processing scenario considering the digital modernization efforts of the United States Military Entrance Processing Command (USMEPCOM). The study evaluates the necessary modifications to current processes, with a particular focus on the influence of two key information technology systems, the MEPCOM Integrated Resource System (MIRS) 1.1 and the Military Health System (MHS) Genesis, on manpower at a Military Entrance Processing Station (MEPS). In doing so, the study establishes baseline processing metrics for assessing these impacts. By utilizing discrete event simulation modeling and leveraging current literature, the study proposes strategies for incorporating technological advancements into …
Techniques For Addressing Extreme Class Imbalance For Artificial Neural Networks Training,
2024
Air Force Institute of Technology
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.
A Multi-Objective Approach To Optimal Deployment Policies For Wireless Sensor Networks Using Drop Points,
2024
Air Force Institute of Technology
A Multi-Objective Approach To Optimal Deployment Policies For Wireless Sensor Networks Using Drop Points, Noah E. Fox
Theses and Dissertations
This research addresses the development of deployment policies for aerially dropped sensors in a wireless sensor network (WSN). Multi-objective genetic algorithm (GA) and simulated annealing meta-heuristic techniques, along with Monte Carlo simulation are used to identify policies with the aim of maximizing coverage and minimizing the number of sensors deployed. The policies developed from these techniques are then compared against uniform sensor distribution, as well as initial deployment policies that focus sensors in the center and edge of the region, as well as evenly deployed over the region. A total of 29 non-dominated policies were identified from the GA and …
An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability,
2024
Air Force Institute of Technology
An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability, Grant R. Engel
Theses and Dissertations
The goal of this paper is to determine an optimal cycle length, in months, that minimizes costs and maximizes availability for deploying units in the United States (US) Army. The US Army must be cost efficient while maintaining the flexibility required to adapt to dynamic mission demand. The current practice is to deploy units for a length between the range of 6 to 12 months; however, this varies from unit to unit and the best policy is not clear. We address these issues by forming a mathematical programming model with unique characteristics that distinguish it from others of similar design. …
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models,
2024
Air Force Institute of Technology
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge
Theses and Dissertations
This research examines China and derives insights specific to it and the First Island Chain and the Second Island Chain. In doing so, this research demonstrates a methodology to examine other competitors and their geostrategic interests. In the first phase of analysis, it develops a value hierarchy to depict objectives within subregions of the area of interest and considers four alternative weightings of the value hierarchy. In the second phase of analysis, it applies four location-covering models to assess how the competitor would emplace a range of limited resources to deter and/or control points of interest. Results indicate that land-based …
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment,
2024
Air Force Institute of Technology
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
Theses and Dissertations
The role of autonomy has evolved recently, demanding tighter integration between human and autonomous systems, particularly in highly contested A2AD environments. Near-peer adversaries have modernized their integrated air defense systems (IADS), diminishing the current advantages of the United States Air Force. To regain air dominance, efforts like the Collaborative Combat Aircraft (CCA) program are underway, aiming to deploy unmanned autonomous alongside manned next-generation fighter aircraft. This research assesses various operational concepts, focusing on autonomous tactics post-manned fighter loss, strike timing of independent teams, and weapon configuration observability. Using the Advanced Framework for Simulation, Integration and Modeling (AFSIM), an agent-based model …
Usmepcom Prescreens: A Value-Focused Thinking Approach,
2024
Air Force Institute of Technology
Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig
Theses and Dissertations
As the recruiting crisis continues to impact the United States Armed Forces, the United States Military Entrance Processing Command (USMEPCOM) continues to search for ways to increase its capability to efficiently determine which applicants are suited for military service. Unfortunately, USMEPCOM does not have a way to evaluate newly suggested alternatives. By leveraging Value-Focused Thinking (VFT), this research describes 28 fundamental objectives that can be applied to a variety of current and future decision problems. Further, this research applies these fundamental objectives to analyze a current decision problem: reengineering the prescreen process to decrease the time from prescreen submission to …
Analysis And Visualization Of Military Convoy And Supply Utilization In Support Of State-Specific Freight Plans,
2024
Air Force Institute of Technology
Analysis And Visualization Of Military Convoy And Supply Utilization In Support Of State-Specific Freight Plans, Madison E. Hofmann
Theses and Dissertations
Researching the United States military’s use of highways and interstates is necessary for the allocation of federal infrastructure spending. As of today, there is no established method, tool, or process routinely utilized by the Surface Deployment and Distribution Command to effectively show a system-wide view of military convoy and supply route usage across CONUS. This research advances this endeavor by providing a by-state characterization of interstates and major highways in 2022, categorized by the volume of military freight they support. Some notable methodologies used to conduct the analyses are the shortest path problem, map matching algorithms, and Global Positioning System …
U.S. Army Cadet Command Branch Prediction Model,
2024
Air Force Institute of Technology
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
Theses and Dissertations
The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.
A Reinforcement Learning Self-Play Approach For Informing Wargaming Analysis & Development,
2024
Air Force Institute of Technology
A Reinforcement Learning Self-Play Approach For Informing Wargaming Analysis & Development, Kathleen A. Maclean
Theses and Dissertations
The integration of RL into wargames to learn strategic and operational insights is of interest to the United States Air Force. This thesis explores the application of a RL SARSA(λ) algorithm to the wargame Stratagem MIST. The primary objective is to select air and ground combat policies for the Blue Agent to effectively counter various opponent strategies across different terrains. This testing enables a comprehensive evaluation of the Blue Agent’s adaptability and performance under varying combat conditions. The use of basis functions, linear value function approximations, and specific air and ground strategies simplifies the state and action spaces of the …
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness,
2024
Air Force Institute of Technology
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
Theses and Dissertations
This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …
Factors Affecting The Retention Of Active Duty Airmen,
2024
Air Force Institute of Technology
Factors Affecting The Retention Of Active Duty Airmen, Gregory A. Picardi
Theses and Dissertations
This study investigates the factors influencing the early exit of active duty Airmen, particularly in the context of the recruitment challenges faced by the USAF in Fiscal Year 2023. The research highlights the significant impact of the implementation of MHS Genesis, a healthcare administration program, on recruitment processes and the broader issues affecting military recruitment, including physical and emotional trauma concerns among potential recruits. Through a survey conducted at Wright-Patterson Air Force Base involving 251 participants, the study utilizes a Chi-square test to explore the primary and secondary reasons for leaving active duty, with family pressure, stability, and financial reasons …
Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity,
2024
Air Force Institute of Technology
Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity, Daniel L. Straw
Theses and Dissertations
This research examines the operations of behavioral health clinics in Air Force Continental United States facilities, with a focus on operational efficiency and capacity through Data Envelopment Analysis. Most facilities operate near capacity, demonstrating efficient resource usage, but concerns arise with the potential of increased demand. Days-to-care and leakage are identified as major sources of inefficiency, suggesting that addressing these issues can enhance operations and reduce costs. The study also explores the impact of COVID-19 on care delivery.
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem,
2024
Air Force Institute of Technology
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
Theses and Dissertations
This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …
Navigating Complex Environments: A Comparative Study Of Shortest Path Algorithms For Military A2ad And Civilian Obstacle Avoidance,
2024
Air Force Institute of Technology
Navigating Complex Environments: A Comparative Study Of Shortest Path Algorithms For Military A2ad And Civilian Obstacle Avoidance, Ebony N. Williams
Theses and Dissertations
This thesis investigates advanced navigation in complex environments for urban and military applications, focusing on overcoming obstacles through algorithms like Dijkstra's. It highlights the role of adaptable cost functions in customizing strategies for different scenarios. The research identifies effective algorithm-cost function combinations, improving route planning and safety in civilian and defense sectors. It advances pathfinding knowledge and sets the groundwork for future enhancements with Python simulations and AFSIM.
Optimizing Deployment Kit, Introducing Acceptance Threshold,
2024
Air Force Institute of Technology
Optimizing Deployment Kit, Introducing Acceptance Threshold, Saleh A. Alshuhri
Theses and Dissertations
This article addresses the optimization of a specialized deployment kit crucial for air force operations, emphasizing the need for rapid and efficient aircraft deployment. Through a comprehensive analysis of historical data, the study aims to streamline the kit's contents without compromising effectiveness. The research suggests a potential reduction of 7.8%, with an impressive 60.5% decrease if a minimal 5% threshold is deemed acceptable. While the primary focus is on air force deployment, the broader implications extend to military entities, such as armies and navies, highlighting the applicability of the findings in enhancing deployment efficiency across various defense sectors.
Evaluation Of Vtol-Capable Cargo Uavs For Dispersible Airfield Logistics In The Usindopacom Aor,
2024
Air Force Institute of Technology
Evaluation Of Vtol-Capable Cargo Uavs For Dispersible Airfield Logistics In The Usindopacom Aor, Maria C. Chedzoy
Theses and Dissertations
This research models and analyzes the ability of commercial cargo UAVs to rapidly evacuate logistics from an airfield to proximal, outlying destinations, particularly in the USINDOPACOM AOR. This is a tenet of Agile Combat Employment by the USAF, which seeks to mitigate the effect of kinetic threats by near-peer adversaries. The analysis sets forth a binary linear program to minimize the total time to evacuate a fixed amount of logistics from an airfield. Parameters include the cargo UAV with its performance specifications, number of cargo loading points at the airfield, number of destinations for cargo evacuation, and subset of destinations …
Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability,
2024
Air Force Institute of Technology
Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability, Taylor D. Markham
Theses and Dissertations
Neural networks, despite their prowess in computer vision, often exhibit "flickering". Flickering occurs when networks fail to maintain consistent object representation across frames, leading to inaccurate and inconsistent output. This problem is particularly critical in mission-surety applications where reliable object recognition is crucial. This research presents a novel approach that combines existing object detection and tracking algorithms like YOLO and SORT with a Bayesian backend model. This Bayesian backend incorporates probabilistic reasoning to analyze the network's confidence in its predictions and infer the most likely object identity across multiple frames, effectively reducing flickering and enhancing robustness.
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence,
2024
Air Force Institute of Technology
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
Theses and Dissertations
A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …
