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Unmanned undersea vehicles (UUVs) are being built to meet the growing demand for undersea operational awareness and payload delivery, but there are no tools for operational planners to handle logistics regarding UUVs, such as routing logistic vessels to UUVs of differing capabilities, unique requirements, and time constraints. The feasibility of UUV employment in this manner would result in lower risk and broaden the scope of mission sets for our limited arsenal of manned submarines. Current models seek to optimize a single logistic vessel’s pathing to a group of surface vessels. However, there are no models that seek to provide a global solution with multiple logistic vessels to multi-echelon UUVs. We generalize a prior model for medical evacuation with multiple types of unmanned vehicles using time-synchronized systems via a multiple-path orienteering problem. We extend that model to an undersea logistics problem in a contested environment, with heuristics to approximate global solutions. A successful model provides feasible solutions to a simple scenario for proof of concept and possible real-life scenarios.

The employment of Long-Range Fires is a high priority for the U.S. Navy, addressing the capability of forces to coordinate an emerging arsenal of deep strike weapons that can be launched from an array of joint assets against critical enemy assets at sea or hardened facilities on land. Additionally, the Long-Range Fires process must be resilient in a degraded or denied environment. However, coordinating Long-Range Fires encompasses a complex set of actions, to include target prioritization and development, command and control, tasking, kinetic and non-kinetic fires, battle damage assessment, rearming and contested logistics. Our approach leverages recent Navy-sponsored MBSE research, simulation, and analysis to include projects involving joint fires within Distributed Maritime Operations and feasibility of deploying hypersonic missiles on U.S. surface ships. We apply a similar approach here but augment it with a system of systems analysis of Long-Range Fires in a degraded and denied environment as part of a timely and relevant joint operational scenario. We examine significant design decisions and operational parameters, as well as appropriate measures of effectiveness, in generating successful Long-Range Fires through systems architecture development and simulation analysis.

Despite recent advances in the representation of logistics considerations in DOD staff training and wargaming simulations, logistics information systems (IS) remain underrepresented. Unlike many command and control (C2) systems, which can be integrated with simulations through common protocols (e.g., OTH-Gold), many logistics ISs require manpower-intensive human-in-the-loop (HitL) processes for simulation-IS (sim-IS) integration. Where automated sim-IS integration has been achieved, it often does not simulate important sociotechnical system (STS) dynamics, such as information latency and human error, presenting decision-makers with an unrealistic representation of logistics C2 capabilities in context. This research seeks to overcome the limitations of conventional sim-IS interoperability approaches by developing and validating a new approach for sim-IS information exchange through robotic process automation (RPA). RPA software supports the automation of IS information exchange through ISs’ existing graphical user interfaces. This “outside-in” approach to IS integration mitigates the need for engineering changes in ISs (or simulations) for automated information exchange. In addition to validating the potential for an RPA-based approach to sim-IS integration, this research presents recommendations for a Distributed Simulation Engineering and Execution Process (DSEEP) overlay to guide the engineering and execution of sim-IS environments., Major, United States Marine Corps, Approved for public release. Distribution is unlimited.

NPS NRP Technical Report, The SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover., N3/N5 - Plans & Strategy, This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp, Chief of Naval Operations (CNO), Approved for public release. Distribution is unlimited.

NPS NRP Technical Report, The employment of long range fires is a high priority for the U.S. Navy. Long range fires address the capability of U.S. forces to coordinate an emerging arsenal of deep strike weapons against enemy targets that can be launched from an array of joint assets, to include submarines and surface ships, land platforms, and airplanes. This concept means launching ballistic missiles, cruise missiles, and hypersonic missiles from multiple, extremely dispersed locations, against critical enemy assets at sea or hardened facilities on land. Additionally, the long range fires process must be resilient in a degraded or denied environment. Coordinating long range fires encompasses a complex set of actions, to include target prioritization and development, command and control, tasking, kinetic and non-kinetic fires, battle damage assessment, rearming and contested logistics. Our approach leverages experience gained by applying systems engineering and analysis to recent research involving the operational Navy, to include projects involving joint fires within Distributed Maritime Operations and the deployment of hypersonic missiles on a current U.S. Navy surface ship. We apply a similar approach here but augment it with a system of systems analysis of applying long range fires in a degraded and denied environment as part of a timely and relevant joint operational scenario. We will examine significant design decisions and operational parameters, as well as appropriate measures of effectiveness, in generating successful long range fires through systems architecture development and simulation analysis., N2/N6 - Information Warfare, This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp, Chief of Naval Operations (CNO), Approved for public release. Distribution is unlimited.

NPS NRP Technical Report, Naval platforms are inherently multi-mission - they execute a variety of missions simultaneously. Ships, submarines, and aircraft support multiple missions across domains, such as integrated air and missile defense, ballistic missile defense, anti-submarine warfare, strike operations, naval fires in support of ground operations, and intelligence, surveillance, and reconnaissance. Scheduling and position of these multi-mission platforms is problematic since one warfare area commander desires one position and schedule, while another may have a completely different approach. Commanders struggle to decide and adjudicate these conflicts, because there is plenty of uncertainty about the enemy and the environment. This project will explore emerging innovative data analytic technologies to optimize naval resource allocation and replanning across mission domains. NPS proposes a study that will evaluate the following three solution concepts for this application: (1) game theory, (2) machine learning, and (3) wargaming. The study will first identify a set of operational scenarios that involve distributed and diverse naval platforms and resources and a threat situation that requires multiple concurrent missions in multiple domains. The NPS team will use these scenarios to evaluate the three solution concepts and their applicability to supporting resource allocation and replanning. This project will provide valuable insights into innovative data analytic solution concepts to tackle the Navy's challenge of conducing multiple missions with cross-domain resources., N2/N6 - Information Warfare, This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp, Chief of Naval Operations (CNO), Approved for public release. Distribution is unlimited.

Distributed Maritime Operations (DMO) requires surveillance and targeting information on enemy warships at a great distance. Current DMO CONEMPS/CONOPS and Navy Strategy on Medium/Large USV operations need to apply to USV/UAV teaming in ISR-T capabilities. In a satellite denied environment, UAVs may be launched from USVs to provide an ISR-T capability, possibly a UAV small enough to fit on an MUSV, with a 200 nm range/24-hour airborne capability. We expect to find how potential C3 concepts for employment and utilization apply to the UOC and what current commercial, defense & government UAV options are available for ISR-T, to include recovery and refueling aboard the USV or recovering at a shore site. Our findings will be able to lead to recommendations in leading to using USV/UAV teaming in the ISR-T arena.

Processes used to collect, process, exploit, and disseminate certain data gathered in undersea environments, specifically intended to support target development, currently do not align with existing joint warfighting authorities, decision-making matrices, information-sharing alliances, vernacular, metrics, data systems, or data formats. Multiple other required future decisions about how best to translate seabed warfare into the joint arena rest on this foundational issue. This study intends to determine how best to physically transmit, archive, manipulate, and reference certain types of intelligence, surveillance, and reconnaissance data. The resulting tasking, processing, collections, exploitation, and dissemination (TCPED) chain must exist in such a way that will enable the data to be equally usable as, and comparable to, other ISR data types that already conform to mature sets of processes recognized across all the Department of Defense, the national Intelligence Community, and many foreign military partners. Project team members envision that this will be a multi-year process, with the first year focusing on comparing and contrasting two types of existing processes. This second year will employ Monterey Phoenix computer simulation capabilities for best fit of information, organization and processes.

The undersea warfare community owns, and will continue to acquire, platforms and capabilities beyond those of the previous submarine community. Given the dwindling of surface resupply capabilities and the advances in communications/denial of communications/distributed lethality, over the decades since the last mature submarine resupply capability existed, how, in future fights, will the undersea warfare community be resupplied, and how will they assist in resupplying other members of the joint theater force? Investigation of how to perform logistics resupply for a small, distributed, agile undersea force in Phase 0-2 environments including how the undersea warfare community will both benefit from, and contribute to, this effort.

Existing data links (e.g., Link 16, CEC, IP based links, etc.) are capable of sharing track data manually via an operator. Once the track data is moved off of its original link it may lose most of its properties. Thus, such a track will be stripped of the required fidelity for employment. Thus, data may not be able to be transferred in a complete form among varying data links. Through analysis of current and potential data link systems, we will be able to posit how such data may be able to be transferred without losing its properties. We expect to find by completing a thorough analysis of the transfer of data between various links we will be able to proffer recommendations on how this transfer may be able to be successfully accomplished. Such a finding would allow operating forces to pass data among various stovepipe data link systems without a loss of data properties. Once this research is accomplished, specific improvements to various data links may be examined.

Includes Supplementary Material, In this current hyperconnected era, many could argue that multifaced daily news events, arranged into univocal storylines, generate effects well beyond the media environment. Empirically speaking, most explorations of media and cyberspace focus discretely on one or the other, parochially missing their potential interaction. More specifically, could negative media events, laced with dueling narratives, aimed at the United States and its interests by other countries on a given day, impact the level of cyber intrusions on U.S. networks the next day? The purpose of this study is to relate today’s recorded cyber intrusions on a U.S. network to yesterday’s media events using statistical regression models as the method of testing for the relationship’s existence. The analysis begins with a broad investigation of all regimes, and then proceeds through specific regime types, before narrowing down to case studies of specific countries. The evidence provided from these models bears out that negative media narratives projected by other countries toward the U.S. generate measurable impacts on the level of ensuing intrusions on U.S. networks. Furthermore, these effects vary in important ways across countries and regime types contingent upon their unique culture, political context, and evolutionary setting., NPS Foundation, Civilian, Department of the Navy, Approved for public release. distribution is unlimited

As a result of the COVID-19 pandemic, Commander, Naval Surface Forces U.S. Pacific Fleet (CNSP) is reevaluating shipboard allowances of medical supplies/Personal Protective Equipment (PPE). We sought to positively impact the response at the ship and fleet levels through an evaluation of notional modifications to supply chain practices, thus increasing resiliency for the next pandemic. An exploratory sequential mixed-method research design was used to address two research questions. (1) How can the Navy provide frontline subject matter experts the means to efficiently and accurately track PPEs during COVID-19 type pandemics? (2) How can the Navy determine a reasonable onboard allowance for pandemic-related PPE (given limits on shipboard storage and Authorized Medical Allowance List [AMAL] composition), and an empirically-sound prediction for the usage rate of each category of PPE during a pandemic? The findings identified potential areas of improvement in training, operational supply chain practices, and on-hand N95 mask DDG inventory. To establish viable PPE supply chain management practices, we illustrate how enhanced data standards might be implemented using an optimized Re-order Point (ROP) model, as well as a sustainment of AMAL review boards. We also offer that supply chain training for corpsmen might offer potential improvement to overall shipboard inventory., Lieutenant, United States Navy, Approved for public release. Distribution is unlimited.

In the wake of the 2020 COVID-19 pandemic Commander, Naval Surface Forces U.S. Pacific Fleet(CNSP) is reevaluating current shipboard allowances of medical supplies/Personal Protective Equipment, PPE (masks, gloves, etc..), and disinfecting supplies (hand sanitizers, disinfecting solutions). This study seeks to positively impact COVID-19 response at the ship and fleet levels through an evaluation of notional modifications to authorized medical and shipboard allowances and prepositioned stocks to increase resiliency for the next pandemic. To accomplish this, we intend to first gather policy data concerning required shipboard Authorized Medical Allowance List (AMAL), including consumable supplies which are presently tailored toward mass casualty situations, not pandemics. We will review OPNAV, PACFLT, Fleet Forces, and Navy Medicine guidance regarding pandemic preparedness and response and will integrate our findings with the AMAL review process. We will also gather current CNSP After-Action Reports and Lessons Learned arising from the COVID-19 pandemic to understand likely usage rates and compare them with on-hand inventory. We will also examine available shipboard storage and determine how best to plan for both mass casualties and a future pandemic. We will also examine available pre-positioning sites and their storage availability to determine how best to leverage shore supply storage assets. This effort will then be explored and informed using either mathematical based, stochastic simulation or a multi-variate optimization leading to improved understanding and identification of weak and strong areas in our on-hand inventory readiness as well as our ability to accomplish recommended levels of resupply. Our findings will result in recommended minimum requirements for on-hand shipboard inventory (PPE, disinfectant, etc.) and determine the best pre-positioning of larger stocks ashore which can then be delivered to and sustain fleet assets though an extended pandemic. T

The military's identification of an information environment that is somehow separate from the traditional operational environment has created organization, theoretical, and philosophical schisms in how we think about military operations. This divide has resulted in recognized gaps and overlaps in how the military plans and executes operations; basically, although still highly effective, the military is recognizing that they are becoming highly inefficient in blending information (non-kinetic) options with traditional (kinetic) options. This research is designed to help identify the gaps and overlaps in planning and employment of non-kinetic and kinetic approaches to reaching operational objectives. The intent is to independently analyze behavioral objectives based on mission sets and the means by which they may be achieved. The use of kinetic weapons and projectiles is well understood among military planners, therefore this means-analysis will focus on the extensive number of options available that function via the phenomenon of information (i.e., non-kinetic delivery.). The results will be a matrix of options for matching means-to-objectives that will help inform operational planners in developing effective and efficient allocation of resources.

The Naval Aviation Enterprise (NAE) is tasked with providing sufficient numbers of aircraft to meet readiness and operational surge requirements. This study seeks to improve readiness as defined by the number of mission-capable aircraft available for operational tasking. Our research is focused on the output of the Aviation Depot Readiness Availability Model (ADRAM) and seeks to inform Senior Leaders with regard to funding decisions about aircraft maintenence. Our hope is to reduce the risk of decreased aircraft readiness as well as explore the available trade space and implications of potential alternatives, revealing some practical outcomes. This project also seeks to optimize ADRAM’s ability to balance readiness with cost . The far-reaching implications of programmatic decisions concering maintaining aircraft airframes through the examination of “operational availability” is a complex problem that requires exploration. Through our examination of ADRAM output over numerous maintenance events, many depots, and many types, models, and series (T/M/S) of aircraft, we found that ADRAM does not produce the most efficient and effective set of maintenance events required to meet a specific readiness goal (or to implement a decrease in funding). There are two basic reasons for this observation: some of the model’s solution algorithms do not appear consistent with current practices, while other algorithms reflect current practices that are likely to be suboptimal. In particular, we observe that ADRAM assumes that the most expensive tasks are the first to be deferred. This does not necessarily minimize the impact of the delay on readiness. Also, ADRAM assumes that “level-loading” of maintenance tasks occurs across facilities. This may be consistent with current practices but does not necessarily allocate events to the most efficient facilities. Furthermore, ADRAM assumes “fair-sharing” of budget cuts across facilities. This does not necessarily defer events away from the lea

Through the successful application of data analysis tools, beginning-to-end analysis of enterprise supply and maintenance chains has resulted in the identification of valuable optimization opportunities. With complex logistics systems generating large amounts of structured and unstructured data, an enterprise’s ability to analyze and interpret mass amounts of disparate datasets is a source of great competitive advantage. The U.S. Navy operates a complex global logistics system, and its ability to optimize its operations is critical to ongoing success. In our research, we focus data analysis methods on critical weapon systems such as the CH-53E aircraft in order to identify unseen relationships in their sustainment efforts using reported maintenance records fielded by naval component commands and the multiple CH-53E subordinate and supporting organizations. Using available analytics tools, we improve our understanding of the relationships among total flight hours, maintenance actions, and percentage of equipment not in service to the aircraft’s Mission Capable rates. We find opportunities to improve the Navy’s understanding of its supply and maintenance practices in support of selected critical weapons systems through the holistic analysis of datasets at the organizational level. We identify pitfalls in weapon system sustainment, thus improving the effectiveness of naval aviation and the operational availability of its aircraft.

Increasing operational availability through logistics readiness is critical to ensure maximum safety and capability of forward deployed ships. The increased operational tempo that forward deployed ships encounter causes high impact failures that affect operational availability and the commander’s decision-making process. As forward deployed 7th fleet ships encounter casualties, unacceptable operational impacts such as out-of-stock or “redlines” occur. Repair rates may be improved by leveraging predictive analytics to understand failure data. Critical systems such as propulsion, communications, or radar are our primary focus. Areas for optimization are identified through statistical analysis of data that we have collected from 7th Fleet organizations such as NAVSUP, NAVSUP WSS, NAVSEA, CTF-73, and SURFGRUWESTPAC. We analyzed and sorted the data to identify any trends and risks that may not currently be tracked. We assessed distributions of potential failure rates, and a peacetime model was developed using Lexical Link Analysis, and then analyzed used JMP. Using our model, potential situations are built to analyze the output and optimize wait times within the constraints of manpower, funding, and availability. Future work using this model may include efforts to evolve to Phase II operations, with increased naval presence resulting in increased sustainability needs, simulated in SIMIO to test casualty and repair rates, as is shown in sample models in this thesis., N4, Lieutenant Commander, United States Navy, Approved for public release. distribution is unlimited

Cybersecurity relies on Security Operations Center (SOC) personnel to conduct data triage on large numbers of automated alerts to identify true threats to networks. To achieve this goal, SOC personnel must not only filter out false positives in data streams but also coalesce disparate pieces of data to generate information that yields a conclusion of an existing exception condition in the desired state of cybersecurity and requires action. Additionally, false negatives in data streams may later be identified when a compromise is discovered via human reporting or other means. Limitations of Turing machines used as automated sensors, ever-increasing network size and speed of transmission, limited numbers of qualified personnel, and the necessity to work in uncertainty all serve to exacerbate the continual condition of information overload for network defenders. This research will attempt to address information overload by reducing the information that is presented to personnel working in a SOC. The goal is to propose a new framework for determining cybersecurity risk as a time-dependent function, which will allow for reduced information overload and at least maintain equivalent cybersecurity posture. Our findings indicate that the quantity of information presented to cybersecurity personnel can be reduced, in some cases by more than half, while maintaining the cybersecurity posture required for the completion of mission-essential tasks., Commander, United States Navy, Approved for public release. distribution is unlimited

Recruiting duty is considered to be one of the most challenging billets within the Marine Corps, and Marines will endure great personal hardship to succeed in accomplishing their mission. A subset of this mission is to recruit applicants for the Marine Corps field bands via the Musician Enlistment Option Program (MEOP). This portion of the recruiting mission is considered one of the most difficult to accomplish. It is also the only mission within Marine Corps enlisted recruiting that requires an exclusively tested skill prior to enlisting. Understanding the demographics of this population is key to the Marine Corps effectively targeting its manpower and advertising resources to recruit well-qualified individuals to serve as Marine musicians. Utilizing data management and software analysis techniques, this study examines those previously recruited via the MEOP by mining historical data obtained from the Marine Corps Recruiting Information Support System (MCRISS). It quantitatively describes the population and identifies significant demographic shifts. After identifying statistically relevant changes, this study provides recommendations on how to improve the MEOP recruiting process for greater effectiveness and efficiency. This study’s approach and methodology may be applied to future populations of interest with pre-existing special knowledge, skills, and abilities., Marine Corps Recruiting Command, Outstanding Thesis, Major, United States Marine Corps, Approved for public release. distribution is unlimited

The Navy’s family of Unmanned Surface Vessels (USV) needs rational, robust and repeatable certification criteria for Collision Regulation (COLREGS) compliance, antitamper, environmental and general safety to ensure their readiness for worldwide operational employment. The Navy has no clear-cut guidance of determining whether an autonomous surface vessel is ready for deployment. Areas to be considered include COLREGS, physical and cyber security, and environmental/safety issues.

Over the course of several years, the Department of Homeland Security (DHS) has worked diligently to improve the quality of its leaders. Such efforts have focused almost exclusively on initiating or expanding programs related to leadership development. To date, the impact of that exertion might be charitably described as tepid. While the issues associated with existing leaders have received ample attention, the selection process that precipitated them has not. This gap represents an opportunity to explore a nascent space and suggest new solutions that target the problem at the source. This thesis examines the process of leadership selection at a network level and finds several systemic problems related to measurement, structure, and decision-making. These problems bear a striking resemblance to those observed in the intelligence community and its ability to accurately predict complex future geopolitical events. One method that has dramatically improved the accuracy of geopolitical predictions is superforecasting. At its core, leadership selection is a prediction or a forecast. It is an educated but nonetheless imperfect best guess about how a candidate observed today will perform tomorrow. These features collectively suggest a novel question. Could DHS use a superforecasting methodology to improve its leadership selection process? This thesis follows the progression of that question to an unexpected destination and offers several concrete recommendations., http://archive.org/details/shootthehorseand1094561354, Outstanding Thesis, Civilian, Department of Homeland Security, Approved for public release; distribution is unlimited.

The purpose of the Navy Supply Corps is to manage the logistical pipeline, so resources are delivered to the warfighter as required. Naval units are easily able to replenish their stores while they are in port, but difficulties arise when they get underway to conduct missions and training exercises. The use of unmanned systems introduces a new naval unit class with many beneficial characteristics, including autonomous control for which minimal human supervision is required, reliability demonstrated by COLREGS, and spacious cargo transportability evidenced by enough topside space for a 20-ft. ISO container. This thesis seeks to identify key influential factors and provide useful insights to logistically support naval readiness and the naval units’ continued ability to complete their missions. Modeling and analytical innovations used in this research include implementation of a discrete event simulation program, use of design of experiments, and sophisticated statistical analysis. Results from the analysis indicate that the rate of new requests, USVs’ top speed, and number of USVs have the most impact on turnaround times for both mission duration and request fulfillment. Properly utilized, USVs can be a strong contributor to the success of U.S. Navy missions., http://archive.org/details/unmannedsurfacel1094562270, Lieutenant Commander, United States Navy, Approved for public release; distribution is unlimited.

Within the human-machine relationship, distrust can arise. The Department of Defense utilizes automation, autonomous systems, and artificial intelligence to reduce cognitive workload and improve mission capabilities; however, adoption rates of autonomous unmanned surface vehicles (USVs) remain low. This thesis asks how human distrust of machines and machine learning relates to adoption rates. First, we identify trust components by building upon a model created by Gari Palmer, Anne Selwyn, and Dan Zwillinger in 2016. Then, we identify components that apply to the military environment that could affect the adoption rate such as smoothing time, policies and regulations, competition, robustness, understandability, subjective norm, human interaction, policy effect, risk to force, time sensitivity, war, time between wars, and catastrophic failure. Through S-curve and smoothing modeling, we find that trust components can be quantified in the human machine relationship as positive or negative trust, and that a relationship exists between understandability and adoption. While autonomous system components generally undergo rigorous testing to verify suitability and operability, human-machine trust is not usually incorporated into design and testing phases. When trust is built into the design and acquisition process, adoption of autonomous USVs is more likely to increase. Researchers can apply our trust model to future autonomous systems to mitigate distrust and human-machine teaming., http://archive.org/details/trustandundersta1094563429, Lieutenant, United States Navy, Lieutenant Commander, United States Navy, Approved for public release; distribution is unlimited.

The Navy has identified a need to exploit the benefits of artificial intelligence, particularly in the realm of common tactical picture (CTP), combat identification (CID), and battle management aids. Of grave concern to the Navy is the CID of unknown unknowns or things that are not known to exist or can be easily tracked. Artificial Intelligence, and related machine learning, deep learning, and deep analytics tools, provides a technology that can be used to assist commanders in processing information to help determine these unknown unknowns. The limitations of current CID systems, coupled with the increased amount of the sensor data overloading current watch-stander's abilities to determine regularized patterns and anomalies, offers a technological opportunity to exploit and ease a human’s workload. Machine learning and other AI systems may be able to fill this gap, assisting in the determination of unknown-unknowns. Research conducted on machine learning and deep learning techniques determined possible applications for CID use in the surface Navy, with technology acquisition and integration as the limiting factors. Continued research concerning the integration of legacy systems and new technologies is necessary to realize the true potential of AI in CID unknown-unknown applications., N2/N6 - Information Warfare, http://archive.org/details/artificialintell1094563521, Captain, United States Marine Corps, Approved for public release; distribution is unlimited.

This thesis seeks to study artificial intelligence (AI) technologies that can improve decision-making in complex military tactical environments. Tactical environments can become highly complex in terms of threats, the tempo of events, the element of surprise or unexpected events, the limits of battlespace awareness, and the potential deadly consequences. This type of environment translates into a highly challenging decision space for tactical warfighters. Tactical decision-making tasks quickly surpass the cognitive abilities of humans in terms of identifying decision options, weighing the relative value of numerous options, calculating the predictive success of options, and performing these tasks under extremely short timeliness. The Navy has identified the need to develop automated battle management aids (ABMA) to support human decision-makers. The concept is for ABMAs to process large amounts of data to develop battlespace knowledge and awareness and identify and prioritize warfare resource and course of action options. Recent developments in AI methods show promise as a critical enabler of ABMAs to support tactical decision-making. This thesis studies AI methods with an objective of identifying specific applications to the realm of tactical decision-making., N2/N6, http://archive.org/details/artificialintell1094562722, Lieutenant, United States Navy, Approved for public release; distribution is unlimited.

Supply Chain Vulnerability Identification Using Big Data Techniques

Unmanned Surface Logistics Concept of Support

Aviation Depot Maintenance Throughput Optimization

Thirty-three local emergency medical services (EMS) authority agencies serve the 58 counties in California. A local EMS authority (LEMSA) in California governs either EMS providers in a single county or several counties combined. Each LEMSA dictates widely different treatment and transport protocols for its paramedics. Preliminary data for this thesis substantiate previously published literature, which shows broad disparities in prehospital care and patient outcomes among LEMSA jurisdictions in California. Although previous research has established the problem of geographic EMS disparities, nothing definitively explains their cause. This thesis contends that the decentralized LEMSA system is the chief culprit for EMS disparities in California, based on an analysis of the available California EMS performance-measure data. Regression analysis does not identify a single factor to explain the problem; the only constant across all LEMSAs in California is that their treatment protocols and training standards to maintain local accreditation vary widely. Unfortunately, the striking lack of performance-measure data—a data desert—for EMS throughout the United States limits the scope of research seeking to explain the inconsistency in EMS care.

The U.S. Navy competes for resources in an economically constrained environment. With heavy financial obligations, it must look for alternative approaches to communicate that can continue to enable its forces to carry out operations. This research investigates the viability of potential communication options used in a communications-degraded environment. Technological advancements in the area of wireless mesh networks and mobile ad hoc networks (MANETs) have demonstrated a high level of success in facilitating communication. One similar technology, the aerial layer network (ALN), is gaining momentum throughout the U.S. armed forces as an alternative to satellite communications. In our virtual model simulations, we created a carrier strike group (CSG)-level MANET that received data packets from a ground site via an ALN without satellite connectivity, and communicated over a distance greater than 800 nautical miles. To determine network performance, generally accepted network reliability axioms were utilized. Our network simulation demonstrated a MANET and ALN are viable communication solutions for a CSG in a command and control denied or degraded environment. We evaluated mobile IP routing protocols Optimized Link State Routing and Ad Hoc On-Demand Distance Vector (AODV) routing and determined that AODV provided better packet delivery performance.

In this research, a Microsoft Excel retirement planning decision support tool is developed to help Marine Corps Officers visualize their own time horizon to achieve retirement. Basic and advanced features allow individuals to adjust all of the variables within the tool to represent their unique financial retirement goals. The decision support tool will also help eligible Marine Corps Officers to choose between the Legacy Retirement System and Blended Retirement System, by understanding each retirement plan and calculating which plan would provide more financial benefit.

What drives resilience for states in the United States? This thesis seeks to answer this question and addresses the absence of quantitative metrics for efforts to increase resilience. We used a literature review to create a framework of indicators, a Delphi review to validate the framework, and statistical techniques to create a composite indicator from the framework. Knowing that all models are false but some are useful, the intent was not to perfectly predict resilience, but simply to create a tool to help practitioners understand which programs most affect resilience. Our results showed that even programs assumed to have a strong link with resilience—such as strong building codes—actually had little relation with increased resilience, as measured by weather-related fatalities and economic losses. Some conclusions are that state-level measurement masks granular differences that are important in understanding weather-related deaths. In most states, for instance, weather-related deaths happen infrequently, yet most federal and state programs aim to increase resilience in catastrophic events, which makes it challenging to validate resilience measurement tools using data on day-to-day deaths. Recommendations include that FEMA should continue to build on improvements to the Threat and Hazard Identification and Risk Assessment and that agencies should determine and consistently measure the dependent variable (i.e., deaths and damage or other measures of well-being)., http://archive.org/details/measuringstatere1094560383, Civilian, Department of Homeland Security, Approved for public release; distribution is unlimited.

Reissued 3/14/2019 with corrections to the abstract., The increasing emphasis on innovation and the massive amounts of data that are collected and stored from innovative platforms provide for an opportunity to analyze innovative ideation data sets. However, the Department of the Navy does not currently have an effective way to analyze and visualize the innovative ideations posted on their idea-producing platforms. At present, big data analysis is conducted by humans who manually sift through each ideation posted and are virtually unable to fully depict and connect multiple and mutual ideas. This current process is very labor intensive and time consuming, and is subject to human error. This thesis assists in this endeavor by providing a proof of concept through use of Lexical Link Analysis (LLA) to analyze a large data set from an innovative ideation platform, the Massive Multiplayer Online War game Leveraging the Internet (MMOWGLI). LLA provides an automated methodology to depict pattern recognition, anomaly detection, and data fusion derived from this dataset. Through the use of LLA, MMOWGLI data are visually depicted, showing these ideas and how they interrelate as well as revealing emerging themes and patterns that can be more easily understood so that decision makers are presented with increasingly useful and accurate information., http://archive.org/details/oneinamillionfin1094560450, Outstanding Thesis, Captain, United States Marine Corps, Approved for public release; distribution is unlimited.

This research seeks to find root causes of Class A or B mishaps in Navy surface ships in order to identify ships at risk for future mishaps. Additionally, by looking at data from ships that experienced mishaps between 2012 and 2017, and by searching beyond the root cause of specific causal factors for these incidents, we may be able to determine if indicator variables could have predicted the ships were at risk. We explored the LHD, LPD (San Antonio Class), and CG ship classes, as these classes experienced the most mishaps between 2012 and 2017. We used linear regression, descriptive statistics, time-series analysis, and data optimization as the primary methods to examine our collected data. We implemented a reverse-forecasting, or backcasting, approach to correlate variables to LHD, LPD, and CG class ships that experienced a Class A or B mishap in the studied years. We were unable to identify a correlation in the numerous data sets. Small amounts of correlation were found in the data models, but nothing statistically significant that would help predict future mishaps.

Over the course of several years, the Department of Homeland Security (DHS) has worked diligently to improve the quality of its leaders. Such efforts have focused almost exclusively on initiating or expanding programs related to leadership development. To date, the impact of that exertion might be charitably described as tepid. While the issues associated with existing leaders have received ample attention, the selection process that precipitated them has not. This gap represents an opportunity to explore a nascent space and suggest new solutions that target the problem at the source. This thesis examines the process of leadership selection at a network level and finds several systemic problems related to measurement, structure, and decision-making. These problems bear a striking resemblance to those observed in the intelligence community and its ability to accurately predict complex future geopolitical events. One method that has dramatically improved the accuracy of geopolitical predictions is superforecasting. At its core, leadership selection is a prediction or a forecast. It is an educated but nonetheless imperfect best guess about how a candidate observed today will perform tomorrow. These features collectively suggest a novel question. Could DHS use a superforecasting methodology to improve its leadership selection process? This thesis follows the progression of that question to an unexpected destination and offers several concrete recommendations.

The U.S. Marine Corps prides itself on the ability to successfully operate within a dynamic environment in an expedient and expeditious manner. No small accomplishment, this burden lies heavily on supply professionals and the agility of the supply network in support of operating forces. Supply chain management (SCM) must cultivate dynamic supply chains to be as fluid as the maneuver units it supports. Though military action is reactive at times, proactive preparations foster an ability to increase momentum and gain the initiative. This quantitative research assesses the supply chain of a deployed MV-22 Osprey squadron to discover inefficiencies, and makes recommendations to increase supply chain productivity. Specifically, this study takes advantage of Microsoft Excel and big data techniques to sort through structured and unstructured data. By collaborating with the Department of the Navy (DoN) and obtaining data sets, we are able to apply Excel and Lexical Link Analysis (LLA), a text-mining software, to derive relationships between given data sets. Interpreting these relationships helps determine opportunities, shortfalls, and favorable and unfavorable conditions within the MV-22 Osprey supply chain. The results observed can form the basis for supply chain improvement recommendations and can help enhance DoN SCM productivity.