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2024 Vol. 29, #1 - Analysis of a Distributed Command-and-Control Algorithm to Implement Mosaic Warfare
Stephen D. Donnel, Brian J. Lunday, and Nicholas T. Boardman
Recognizing that communication between assets may be possible locally but not globally (e.g., due to disruptions to a communication net-work), Mosaic Warfare requires the movement and operation of multiple, dispersed assets in smaller groups (i.e., tiles), within which exist hierarchical, functional relationships between assets. This research sets forth and evaluates a hierarchical asset tiling and routing heuristic (HATRH) to implement Mosaic Warfare for an enterprise of aerial assets comprised of air-borne sensors, command and control aircraft, and strike aircraft seeking to move toward and destroy a set of stationary targets. The HATRH is comprised of three, iteratively applied algo-rithms: a grouping algorithm to cluster assets into functional tiles, and two algorithms related to group movement and individual asset move-ment, respectively. Embedded within the latter two algorithms are user-determined parameters that roughly correspond to group and individual asset agency within the mosaic. Extensive testing examined the effect of these parameters and asset density for three different operational scenario designs, and with comparison to optimal (i.e., efficient) asset utilization via two price of anarchy (POA) inspired metrics. Results showed the user-defined parameter corresponding to individual asset agency notably influenced both average munition expenditures and the average distance traveled by assets. In the scenario wherein assets initially surround adversary targets, both the individual and group agency user-defined parameters influence operational efficiency, in terms of munitions expended and fuel consumed.
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2024 Vol. 29, #1 - Optimizing Surveillance Satellites for the Synthetic Theater Operations Research Model
Steven M. Warner and Johannes O. Royset
In response to needs of the Synthetic Theater Operations Research Model (STORM), Warner and Royset developed a mixed-integer linear pro-gram for better utilization of surveillance satellites during a simulated theater-level conflict. The program prescribes plans for how satellites and their sensors should be directed to best search an area of operations. It also specifies the resolution levels employed by the sensors to ensure a suitable fidelity of the resulting images. On average, the program yields 55% improvement in search coverage relative to an existing heuristic algorithm in STORM.
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2024 Vol. 29, #1 - From FMECA to Decision: A Fully Bayesian Reliability Process
Andrew N. Hollis, Timothy A. Moore, Alyson G. Wilson, and Nicholas J. Clark
As acquisition processes have evolved in the military, often the reliability testing is still done using traditional techniques. As large, complex systems are developed using multiple vendors, conducting multiple testing using traditional design of experiments is no longer feasible. Andrew Hollis, Tim Moore, Alyson Wilson, and Nick Clark developed a fully Bayesian reliability process that incorporates prior knowledge from the vendors as well as prior experience from system engineers. The results of this study demonstrate that Bayesian methods can enhance current testing procedures allowing for fewer experimental trials during reliability testing.
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2024 Vol. 29, #1 - Optimal Designs for Multi-Response Experiments
Brittany Fischer, Sarah E. Burke, Douglas C. Montgomery, and Bradley Jones
In developmental or operational testing there are usually multiple performance and quality metrics that are of interest in an experiment, but much of the research in designed experiments is focused on having only one response variable. This research provides a general solution to finding a test design for multiple responses that follow different distributions. In test and evaluation, the budget can be limiting due to high test costs. Sequential testing is also not often possible due to the complexity of the tests. Therefore, multi-objective optimization is needed to identify a designed experiment in these test environments.
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2024 Vol. 29, #1 - Revealing Bridges in Social Networks
James Andrew Leinart and Richard F. Deckro
For various reasons, social network and group components may be unrevealed. In a terrorist network, a military/security organization attempting to dismantle the network is unlikely to know all individuals and/or their interactions and roles. The ability to characterize and detect key individuals that connect network groups, i.e., bridges, could be valuable in the national security structure’s efforts. This research develops a statistical method to identify which individual(s) in social network groups are bridges, and infer the existence of a bridge from group data that does not contain information about the bridge or its contacts. Additionally, an approach for recreating the ground truth net-work once a bridge’s existence has been detected is presented.
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2024 Vol. 29, #2 - Simulating MQ-9 Aircrew Training to Determine Throughput, Identify Bottlenecks, and Optimize Manning Levels
Lance E. Champagne, Thomas P. Talafuse, and Erika E. Gilts
Unprecedented demand for unmanned aerial systems (UAS) by the United States Air Force (USAF) is driving a corresponding demand on training units responsible for producing UAS aircrew. Erika Gilts, Lance Champagne, and Thomas Talafuse developed a simulation of MQ-9 aircrew training to identify throughput bounds and explore process and resource changes affecting training throughput and duration. The results demonstrate that specific changes in staff skill mix and class size are particularly influential in increasing student throughput and may reduce training time. Potential bottlenecks/constraints are identified and indicate novel approaches to course execution to increase the instructor utilization and meet the growing requirements for UAS aircrew. The results are used by 9th Attack Squadron to address instructor skills mix necessary to meet training goals.
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2024 Vol. 29, #2 - U.S. Air Force Aerial Refueling Optimization
Douglas S. Altner, Isaac A. Armstrong, Abby Pusateri, Andrew M. Armstrong, and Robert P. Bennett
This paper presents an optimization model for batch planning U.S. Air Force (USAF) aerial refueling operations—assigning in-air refueling requests to tanker flights. The model contains many constraints and considerations not included in prior publications on this topic, and the approach com-bines graph construction heuristics with integer programming. The authors also present computational results showing how the model automatically generates plans that are better than human-created plans in terms of fuel efficiency and com-parable in terms of number of flights planned.
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2024 Vol. 29, #2 - Validating Multi-Resolution Aircraft Models with Probabilities of Agreement
Matthew C. Ledwith, Raymond R. Hill, Lance E. Champagne, and Edward D. White
Modeling and simulation capabilities help the Department of Defense organize, train, educate, equip, and employ current and future forces across the full range of operations. Within the military analytic domain, validation activities and the study of the appropriateness of modeling is a growing area of professional concern. In this article, a recent functional response validation metric, the probability-of-agreement validation metric, is detailed, which enables informed comparisons between military simulation models and the real-world systems or processes they emulate. Matthew Ledwith, Raymond Hill, Lance Champagne, and Edward White exemplify the probability-of-agreement vali-dation metric through a validation exercise involving the comparison of two multi-resolution, high-fidelity F-16 aircraft simulation models.
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2024 Vol. 29, #2 - The Utility of Machine Learning Applied to Military Assessment and Selection
Hayden Deverill, William Scherer, Michael Porter, and Allan Stam
Special Operations Forces (SOF) military units use a comprehensive assessment and selection (A&S) to acquire the most qualified candidates. One unique challenge is to objectively evaluate the human dimension of attributes such as leadership, resilience, and grit in candidates. This challenge often results in both tangible and intangible costs to the A&S system. The authors present a case study of how applying machine learning methods to historical data collected on candidates who attended a specific A&S provide utility to improving the holistic A&S process. The results aid in the challenge that exists in evaluating candidates in the human dimension by leveraging data to more objectively assess each candidate.
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2024 Vol. 29, #2 - On Several Properties of Uniformly Optimal Search Plans
Liang Hong
The theory of optimal search concerns the optimal way of searching for a target given a limited budget when the target location is uncertain. Since its birth, it has been widely applied in many military (especially naval) and civil search missions. Mastering the optimal search theory will help a military force to win the upper hand in many conflicts, com-petitions, and confrontations. The results of this work are immediately applicable to any real-world search mission.
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2024 Vol. 29, #3 - Development and Analysis of Military Cost-Imposing Actions
Jacob P. Batt, Colton C. Blatchford, Isaac P. Coolidge, Kevin E. Cruz, Glen R. Drumm, Daniel F. Feze, Daniel T. Flynn, Mark A. Gallagher, Norma Ghanem, Alexander J. Hancock, Rhett C. Harms, Brian T. Johnson, Michael M. Maestas, et al
The United States Congress has directed the Department of Defense to investigate strategies that impose significant costs on our adversaries. Air Force Institute of Technology students propose an approach for finding and evaluating cost-imposing actions. They apply risk techniques to identify an adversary’s vulnerabilities to potential United States’ actions along with the adversary’s potential responses to mitigate the impacts of those actions. They conducted a hypothetical demonstration of their approach, where military effectiveness is evaluated with the Bilateral Enterprise Analysis Model (BEAM). Military analysts may apply their systematic approach to investigate and evaluate potential cost-imposing actions.
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2024 Vol. 29, #3 - A Framework for Using Priors in a Continuum of Testing
Victoria R. C. Sieck, Justin Krometis, and Steven Thorsen
A strength of the Bayesian paradigm is that it can leverage all available information—to include subject matter expert opinion and previous (possibly dissimilar) data—through prior probabilities (priors). This article develops a framework for thinking about how differently characterized priors can be appropriately used throughout the continuum of testing. In addition to the application of various priors, the application of the evolution of priors contributes greatly to analytical understanding and will be addressed, considering cases such as when a system’s state significantly changes (e.g., is modified) during phases of testing. The evolution of priors can start with priors attempting to provide no information and evolve toward priors that capture the (newly) available information. This article further discusses priors based on institutional knowledge, as well as those based on previous testing data; the focus will be on previous, in some ways dissimilar, data, relative to a current test event. A discussion on which priorsmightbemorecommonin various phases of testing, types of information that can be used in priors, and how priors evolve as infor-mation accumulates is also included. Finally, a real-world example using the Stryker family of vehicles demonstrates how priors can be employed in a continuum-of-testing construct.
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2024 Vol. 29, #3 - Multilevel Optimization of Military Air-to-Ground Weapon Purchases across Time Segments
David M. Goldberg and Matthew S. Goldberg
The military’s requirement for conventional (non-nuclear) air-to-ground weapons is posed as a nonlinear program. The problem is high-dimensional, with many combinations of air-craft, weapons, and targets. Prior work developed heuristics that reduce the dimensionality of the problem, thereby accelerating solution times. The current research extends that work to multisegment conflicts. A decomposition approach reduces the optimization problem into a set of single-segment subproblems. The overall budget manager sets the weapon procurement budgets for the lower-level managers of the subproblems. The lower-level managers each solve a smaller-scale problem to maximize the utility of expected targets destroyed within their respective time segments.
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2024 Vol. 29, #3 - Adversarial Analysis and Confidence
Madeline A. Stricklin and Aparna V. Huzurbazar
At Los Alamos National Laboratory, part of the Department of Energy’s National Nuclear Security Administration, we are entrusted with the safety, security, and reliability of the U.S. nuclear weapons stockpile. We use adversarial analysis for informing aspects of security for nuclear facilities and whether these facilities are likely to be attacked. This problem is particularly difficult in that decisions must be evaluated and made in an incomplete information space. Madeline Stricklin and Aparna Huzurbazar provide a qualitative overview of the different aspects considered in adversarial analysis and propose a quantitative method that illustrates how attacks can be assessed to determine whether an adversary will proceed with a given attack.
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2024 Vol. 29, #3 - Improvement of SAR Target Classification Using GAN-based Data Augmentation and Wavelet Transformation
Jaeoh Kim, Chulhee Han, Jungman Lee, Woo-Seop Yun, Seojin Lee, Taehoon Yang, Donghyeon Yu, and Seongil Jo
his article considers the synthetic aperture radar (SAR) target classification problems when available SAR images having target labels are limited. To improve the classification performance, the authors propose a learning technique combining data augmentation using generative adversarial network (GAN) models and wavelet transformation. They conduct experiments to investigate the improvement of the proposed learning technique with the SAR images from the moving and stationary target acquisition and recognition data. From the experiment results, the proposed learning technique combining GAN-based data augmentation and wavelet transformation has shown greater improvement in SAR image classification when the available learning data is scarce.
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2024 Vol. 29, #3 - Military Operations Research Society (MORS) Oral History Project: Mr. Franklin McKie
Dr. Bob Sheldon, FS
Franklin McKie was an operations research analyst for the United States Army Center for Army Analysis from 1973 to 2004, where his final role was Chief of the Mobilization and Deployment Division. Frank received two Analyst of the Year Awards at Army Operations Research Symposiums. After retiring from fed-eral service, he taught math at the University of the District of Columbia and at the Bethesda, Maryland, branch of Central Texas College at Walter Reed National Military Medical Center and Bolling/Andrews Air Force Base. His oral history appears in the online version of this issue of Military Operations Research.
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