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Articles 61 - 90 of 602
Full-Text Articles in Operational Research
Systematic Review Of Supply Chain Control Tower Critical Success Factors And Resilience Effects, Clay H. Chaffin
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.
Assessing Adoption Barriers Of Sustainable Packaging In Egypt, Carol Ramses Morgan
Assessing Adoption Barriers Of Sustainable Packaging In Egypt, Carol Ramses Morgan
Theses and Dissertations
Sustainable packaging has become an essential part of business decisions and corporate directions. With the rise of environmental damages due to improper waste management and unsustainable practices, businesses have a major responsibility to analyze their products’ life cycles and redesign them with sustainability in mind. Applying sustainable packaging could save companies large amounts of resources, therefore cutting costs, while also achieving the legal and social duty as a corporation towards society and the environment. Many developing countries, with specific focus on Egypt, have recently focused on legislative and corporate decisions in order to encourage more sustainable practices. Egypt’s new Waste …
Efficient Connectivity Management And Path Planning For Iot And Uav Networks, Amirahmad Chapnevis
Efficient Connectivity Management And Path Planning For Iot And Uav Networks, Amirahmad Chapnevis
Theses and Dissertations
This dissertation explores how to better manage resources in mobile networks, especially for enhancing the performance of Unmanned Aerial Vehicles (UAV)-supported IoT networks. We explored ways to set up a flexible communication architecture that can handle large IoT deployments by making good use of mobile core network resources like bearers and data paths. We developed strategies that meet the needs of IoT networks and enhance network performance. We also developed and tested a system that combines traffic from several mobile devices that use the same user identity and network resources within the core mobile network. We used everyday smartphones, SIM …
Federated Active Learning For Network Intrusion Detection, Matthew D. R. Sauer
Federated Active Learning For Network Intrusion Detection, Matthew D. R. Sauer
Theses and Dissertations
This thesis addresses challenges with detecting attacks on computer networks within a Federated Learning (FL) framework, when labeled instances are few. We explore the integration of active learning (AL) and semi-supervised learning (SSL). AL efficiently uses data that would otherwise be wasted or require substantial time for labeling. SSL provides capacity to train models that have a limited amount of labeled data, by utilizing additional unlabeled data that is available. We show how FL combined with AL or SSL can realize a detection system that adapts and trains quickly to new networks, reducing the total amount of data labeling needed. …
Task Optimization Utilizing Digital Transformation Concepts - Automation Project Execution Via Agile Methodology, Anthony Steven Maiello
Task Optimization Utilizing Digital Transformation Concepts - Automation Project Execution Via Agile Methodology, Anthony Steven Maiello
Theses and Dissertations
Task Optimization via the use of automated process improvements is becoming more widespread as more industries lean into the concepts surrounding digital transformation. This shift also necessitates a complementary adaptation in project management methodologies to support the rapid and ever-changing environment, requirements, and innovations. This thesis examines the effectiveness of Agile methodology in managing digital automation projects, with a specific focus placed on process improvements with systems engineering. It accomplished this by contrasting the original model, designed and derived utilizing traditional project management techniques, with the proposed model which is a direct result of the application of Agile project practices. …
Evaluating The Chief Of Staff Of The Air Force 2016 Initiative To Revitalize The Squadron: A Thematic Content Analysis Of Appreciative Inquiry Mechanisms, John M. Huntz
Theses and Dissertations
A thorough thematic analysis and literature review were undertaken to understand better the integration of AI mechanisms within the Revitalize the Squadron initiative. To facilitate the initiative's implementation, the aim is to provide commanders with practical instances, dimensions, findings, and results. Throughout the coding process, instances of AI’s mechanisms were discovered in the literature. The link between PE and HQR boosted the overall vitality within the squadron, where vitality was determined to be the goal. AI, as a whole, was not found in the literature, but the analysis determined that the Revitalize the Squadron initiative was “Appreciative” in nature.
Improving Deep Reinforcement Learning Methodology For Autonomous Defense And Escort Of Military High-Value Assets, Joseph Liles Iv
Improving Deep Reinforcement Learning Methodology For Autonomous Defense And Escort Of Military High-Value Assets, Joseph Liles Iv
Theses and Dissertations
This dissertation explores the application of machine learning to the control of autonomous unmanned combat aerial vehicles (AUCAVs). In particular, this research applies deep reinforcement learning methodologies to a defensive air combat scenario wherein a fleet of AUCAVs protects a military high-value asset (HVA). A collection of air battle management scenarios along with an original simulation environment and a set of designed computational experiments support the approximation of high-quality decision policies by employing Markov decision processes, approximate dynamic programming algorithms, and deep neural networks for value function approximation.
Network Vulnerability Identification For The Material Routing Problem, Carson G. Long
Network Vulnerability Identification For The Material Routing Problem, Carson G. Long
Theses and Dissertations
This dissertation considers the importance of identifying spatiotemporal vulnerabilities in ground distribution networks and uses operations research methods to formulate models that allow military logistic planners to implement prevention and mitigation measures regarding the routing of personnel, equipment, and supplies in contested Areas of Responsibility (AOR). For optimization models relating to identifying spatiotemporal network vulnerabilities in distribution networks, this work leverages game theory, mixed-integer programming, multi-objective optimization, and metaheuristics to inform mitigation measures for shipment routing. This research has three related components: the first component develops a multi-objective mathematical program to identify spatiotemporal vulnerabilities via myopic heuristic identification, in combination …
Test Problem Generation And Metaheuristic Selection For The Multidemand Multidimensional Knapsack Problem, Matthew E. Scherer
Test Problem Generation And Metaheuristic Selection For The Multidemand Multidimensional Knapsack Problem, Matthew E. Scherer
Theses and Dissertations
This work focuses on instance generation methods for the multi-demand multidimensional knapsack problem (MDMKP). Specifically, instance space analysis (ISA) is used to characterize the landscape of existing instances and validate the novelty of new instances generated with a novel problem generation method, the primal problem instance generator (PPIG). The instance generator is capable of producing feasible, diverse, and challenging instances by directly controlling the problem features. PPIG contributes to the previous collections of instances and is validated through instance space analysis. The research presents an in-depth empirical evaluation of existing solution procedures for the MDMKP. The portfolio of metaheuristics examined …
Resources Based Planning Framework For Infrastructure Maintenance And Rehabilitation Projects, Heba Gad
Resources Based Planning Framework For Infrastructure Maintenance And Rehabilitation Projects, Heba Gad
Theses and Dissertations
Infrastructure maintenance and rehabilitation projects involve activities scattered over a large geographical area (e.g., scattered road segments maintenance, telecom towers maintenance program, etc.). Planning such projects require a resource-based approach that accounts for the implications of resource mobility between activities’ locations in terms of time & cost. Existing scheduling techniques fall short of addressing the unique challenges of the scattered nature of these projects in combination with organization's limited resources availability. To address this need, this research presents a resources-based planning framework for infrastructure maintenance and rehabilitation scattered projects with the objective of enhancing resources utilization achieving time and cost …
Information System Availability Status And Its Impact On Customer Wait Times, Joshua A. Cramer
Information System Availability Status And Its Impact On Customer Wait Times, Joshua A. Cramer
Theses and Dissertations
The Military Personnel Flight relies on Air Force Information Systems, specifically the Defense Enrollment Eligibility and Reporting System (DEERS), to manage the personnel records. When DEERS experiences a failure, then the operational ability of the Military Personnel Flight is affected. This study aims at identifying the impact Air Force information system’s availability status has on customer wait times using linear regression.
The Aerial Refueling Asset Basing And Assignment Problem, Camryn E. Deames
The Aerial Refueling Asset Basing And Assignment Problem, Camryn E. Deames
Theses and Dissertations
With growing tensions in the European theatre and Indo-Pacific theatre, the constraints of aerial refueling impede the missions of Air Mobility Command and USTRANSCOM in their execution of both the National Security Strategy and National Defense Strategy. Introducing and integrating semi-autonomous aerial refueling aircraft is a logical next step due to advantages in endurance, survivability, runway requirements, and fuel offloading capacity. This research frames the Aerial Refueling Asset Basing and Assignment Problem with two model approaches: a baseline model and a fuel shuttle concept model. Whereas the former model considers instances with only manned refuelers or only semi-autonomous refuelers, the …
U.S. Army Force Structure Optimization And Sufficiency Analysis, Francis P. Gargin
U.S. Army Force Structure Optimization And Sufficiency Analysis, Francis P. Gargin
Theses and Dissertations
The United States Army perpetually deploys rotational forces across the globe in support of the National Security Strategy. These forces meet a set of discrete mission demands over an extended time period before redeploying, modernizing, and preparing for the next deployment. The U.S. Army now utilizes the Regionally Aligned Readiness and Modernization Model to execute these cyclical stages for unit deployments. Specific emphasis is placed on aligning forces against a Geographic Combatant Command, which allows units to build readiness and lethality oriented towards the same series of threats, physical terrain, and civilian considerations. This research provides an Integer Programming model …
An Analysis Of Aircraft Maintenance Leading Indicator Metrics To Unit-Level Aircraft Availability Rates, William C. Hardy
An Analysis Of Aircraft Maintenance Leading Indicator Metrics To Unit-Level Aircraft Availability Rates, William C. Hardy
Theses and Dissertations
The purpose of this research is to improve the usefulness of data that is already collected within aircraft maintenance organizations to better identify trends, and outliers, and possibly better explain relationships between leading and lagging indicator metrics. Specifically, this graduate research paper sought to answer two research questions addressing what aircraft maintenance metrics significantly impact aircraft availability, and how to measure those to understand which metrics impact aircraft availability most. The research questions were answered through a comprehensive literature review, and the use of multiple linear regression analysis on data from two specific aircraft maintenance organizations from the same location. …
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Theses and Dissertations
Federated learning (FL) is a budding machine learning (ML) technique that seeks to keep sensitive data private, while overcoming the difficulties of Big Data. Specifically, FL trains machine learning models over a distributed network of devices, while keeping the data local to each device. We apply FL to a Parkinson’s Disease (PD) telemonitoring dataset where physiological data is gathered from various modalities to determine the PD severity level in patients. We seek to optimally combine the information across multiple modalities to assess the accuracy of our FL approach, and compare to traditional ”centralized” statistical and deep learning models.
Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald
Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald
Theses and Dissertations
This research examines the Cadet to Air Force Specialty Code (AFSC) Matching Problem (CAMP). Currently, the matching problem occurs annually at the Air Force Personnel Center (AFPC) using an integer program and value focused thinking approach. This paper presents a novel method to match cadets with AFSCs using a generalized structure of the Hospitals Residents problem with special emphasis on lower quotas. This paper also examines the United States Army Matching problem and compares it to the techniques and constraints applied to solve the CAMP. The research culminates in the presentation of three algorithms created to solve the CAMP and …
Advancing Autonomous Swarm Behavior In A Simulated Anti-Access Area Denial (A2ad) Environment, Chad P. Macwilkinson
Advancing Autonomous Swarm Behavior In A Simulated Anti-Access Area Denial (A2ad) Environment, Chad P. Macwilkinson
Theses and Dissertations
Advancements in modern IADS have bolstered A2AD environments and subsequently degraded the advantages that the Air Force once held, prompting a call to reform the nature of warfare in order to challenge these threats. A solution is weapon swarming technology, which has the ability to overwhelm IADS by engagement of a large numbers of low-cost, but lethal air assets that have autonomous functionalities. This research proposes the application of a four dimensional framework for autonomy to a swarm of cruise missiles. A virtual A2AD environment of two opposing forces is constructed using the AFSIM, wherein a manned bomber seeks to …
Simulating Autonomous Drone Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Alexander L. Martinez
Simulating Autonomous Drone Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Alexander L. Martinez
Theses and Dissertations
Army senior military leaders are invested in acquiring modernized aerial platforms and equipment to augment the U.S. Army’s ability to overcome A2AD threats imposed by modern IADS. A prominent element of this modernization effort is the employment of autonomous drones to defeat IADS threats while minimizing risk to Army Soldiers. This research utilizes a framework for classifying the levels of autonomous capability along three dimensions: the ability to act alone, the ability to cooperate, and the ability to adapt. A virtual combat model, created using the AFSIM, simulates the engagement between an enemy IADS and a friendly formation comprised of …
Inducing Sparsity Within High-Dimensional Remote Sensing Modalities For Lightning Prediction, Grace E. Metzgar
Inducing Sparsity Within High-Dimensional Remote Sensing Modalities For Lightning Prediction, Grace E. Metzgar
Theses and Dissertations
The uncertainty of lightning constantly threatens many weather-sensitive fields where the slightest presence of lightning can endanger valuable personnel and assets. The consequences of delaying operations have incited the research of methods that can accurately predict the location of future lightning strikes from the current weather conditions. High-dimensional remote sensing modalities contain information capable of detecting significant patterns and intensities within storms that could indicate the presence of lightning. This thesis induces sparsity into convolutional neural networks (CNNs) and remote sensing modalities through a combination of regularization and tensor decomposition techniques to call attention to sparse features that are most …
An Approximate Dynamic Programming Approach For Solving An Air Combat Maneuvering Problem With Directed Energy Weapons, Elisha A. Palm
An Approximate Dynamic Programming Approach For Solving An Air Combat Maneuvering Problem With Directed Energy Weapons, Elisha A. Palm
Theses and Dissertations
Performing within visual range (WVR) air combat involves the execution of complex air maneuvers and rapid sequential decision making. The complexity of these decisions can increase even further when including additional weapon capabilities. The advancement of unmanned autonomous vehicle technology and weapon capabilities can help combat the hindrance that comes with human limitations. Autonomous unmanned combat aerial vehicles (AUCAVs) and the implementation of advanced weapon capabilities such as Directed Energy Weapons (DEWs) can prove to be vital in a WVR air combat context. This derives the question – Can AUCAV’s possess the proper artificial intelligence and weapon capabilities to attain …
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Theses and Dissertations
This research modeled and analyzed the effectiveness of different routing algorithms for penetration assets in an A2AD environment. AFSIM was used with different configurations of SAMs locations and numbers to compare the performance of AFSIM’s internal zone and shrink algorithm routers with a Dijkstra algorithm router. Route performance was analyzed through computational and operational metrics, including computational complexity, run-time, mission survivability, and simulation duration. This research also analyzed the impact of the penetration asset’s ingress altitude on those factors. Additionally, an excursion was conducted to analyze the Dijkstra algorithm router’s grid density holding altitude constant to understand its impact on …
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Theses and Dissertations
agement, development, and growth of U.S Air Force assets demand extensive organizational communication and structuring. These interactions yield substantial amounts of contracting and administrative information. Over 4 million such contracts as a means towards obtaining valuable insights on Department of Defense resource usage. This set of contracting data is largely not optimized for backend service in an analytics environment. To this end, the following research evaluates the efficiency and performance of various data structuring methods. Evaluated designs include a baseline unstructured schema, a Data Mart schema, and a snowflake schema. Overall design success metrics include ease of use by end …
Probability Of Agreement As A Simulation Validation Methodology, Matthew C. Ledwith
Probability Of Agreement As A Simulation Validation Methodology, Matthew C. Ledwith
Theses and Dissertations
Determining whether a simulation model is operationally valid requires the rigorous assessment of agreement between observed functional responses of the simulation model and the corresponding real world system or process of interest. This research seeks to extend and formulate the probability of agreement approach to the operational validation of simulation models. The first paper provides a methodological approach and an initial demonstration which leverages bootstrapping to overcome situations where one’s ability to collect real-world data is limited. The second paper extends the probability of agreement approach to account for second-order heteroscedastic variability structures and establishes a weighted probability of agreement …
Optimal Control Of Precision Airdrop Trajectories Using Direct Collocation And Analytical Methods, Edward J. Maxwell
Optimal Control Of Precision Airdrop Trajectories Using Direct Collocation And Analytical Methods, Edward J. Maxwell
Theses and Dissertations
The work herein investigates the preliminary designs of an optimal navigation controller for a scalable cylindrical airdrop system controlled with grid fins in planar motion. Precision airdrop capabilities are desired for a range of military and humanitarian missions. Fielded airdrop systems have not met desired performance objectives, particularly regarding accuracy. Direct collocation and analytical methods were utilized to solve the optimal control problem for the grid fin controlled precision airdrop system examined in this work. The optimal control problem was comprised of two phases: controlled descent and parachute descent. Minimum and maximum ranges for the system under varying wind fields …
Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick
Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick
Theses and Dissertations
This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average …
The U.S. Army Officer-To-Unit Assignment Problem, Andrea L. Phillips
The U.S. Army Officer-To-Unit Assignment Problem, Andrea L. Phillips
Theses and Dissertations
Every two to three years, U.S. Army officers must change duty stations, which entails a selection process based on preferences. Currently, officers are assigned to units using a stable-marriage algorithm. Two impracticalities occur within this process. First, officers are required to submit strictly ranked preferences, not allowing indifference among units. Second, the stable-marriage algorithm does not give flexibility to alternative priorities. This research focuses on two modifications to the current model. First, a mixed integer program is created that allows the user, U.S. Army Human Resources Command, to consider other priorities: unit preferences and maximum officer disappointment. Second, generated data …
Designing A Counter-Iads Drone Swarm: Using Evolution To Evaluate Combat Assumptions Underpinning Drone Swarm Target Assignment, Olin H. Kennedy
Designing A Counter-Iads Drone Swarm: Using Evolution To Evaluate Combat Assumptions Underpinning Drone Swarm Target Assignment, Olin H. Kennedy
Theses and Dissertations
The original research goal was to combine the best techniques in the drone swarm literature and model a functional combat drone swarm that conducts a Suppression of Enemy Air Defense (SEAD) mission. However, the body of literature regarding Drone Swarm Target Assignment (DSTA) does not model enemy counteraction and assumes that the drones’ targets are compliant against destruction. Therefore, a model of enemy counteraction against drone swarms is developed, and Novel DSTA (NDSTA) is proposed to respond to the weaknesses of the current DSTA. Both methods of target assignment are combined with a tunable trajectory generation model, and the performance …
Optimal Scheduling Of Aircraft Test And Evaluation Fleets To Balance Availability For Testing And Training, Sarah E. Hoops
Optimal Scheduling Of Aircraft Test And Evaluation Fleets To Balance Availability For Testing And Training, Sarah E. Hoops
Theses and Dissertations
The 96th Test Wing at Eglin Air Force Base manually schedules a fleet of approximately 26 aircraft to conduct a range of missions over a one-to-two year planning period. This study automates the scheduling process, does so in a manner that optimizes multiple planning goals related to aircraft availability for training, and provides the 96th Test Wing with a software tool for the implementation that can be used by operational analysts within the command. We formulate the scheduling problem as a multiobjective, nonlinear, binary integer math program that seeks to maximize both the lowest percent of time any aircraft is …
Retention Prediction And Policy Optimization For United States Air Force Personnel Management, Joseph C. Hoecherl
Retention Prediction And Policy Optimization For United States Air Force Personnel Management, Joseph C. Hoecherl
Theses and Dissertations
Effective personnel management policies in the United States Air Force (USAF) require methods to predict the number of personnel who will remain in the USAF as well as to replenish personnel with different skillsets over time as they depart. To improve retention predictions, we develop and test traditional random forest models and feedforward neural networks as well as partially autoregressive forms of both, outperforming the benchmark on a test dataset by 62.8% and 34.8% for the neural network and the partially autoregressive neural network, respectively. We formulate the workforce replenishment problem as a Markov decision process for active duty enlisted …
Developing Novel Optimization And Machine Learning Frameworks To Improve And Assess The Safety Of Workplaces, Amin Aghalari
Developing Novel Optimization And Machine Learning Frameworks To Improve And Assess The Safety Of Workplaces, Amin Aghalari
Theses and Dissertations
This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model's robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to …