Journal of Defense Analytics and Logistics

Purpose We study the problem of finding optimal locations for a suite of defense assets in order to protect high-value tactical and strategic infrastructure across a vast geographical area. To this end, we present a multi-type with non-overlapping coverage requirement as an extension to the classical formulation for the maximal covering location problem (MCLP). Design/methodology/approach In our case study, we use open source geographic and demographic data from Canadian sources as inputs to our optimization problem. Due to the complexity of the MIP formulation, we propose a hybrid metaheuristic solution approach, for which a genetic algorithm (GA) is proposed and integrated with local and large neighborhood search operators. Findings Extensive numerical experiments over different instances of the proposed problem indicate the effectiveness of the GA-based solution in reducing the solution time by a factor of ten compared to the CPLEX commercial solver while both approaches obtain solutions of similar quality. Research limitations/implications This research is limited to location planning of defense assets leveraging geospatial data of Canada. However, the diverse Canadian geography is among the most challenging given broad variability in population density and the vast size of the country leading to a large search space having substantial variability in fitness performance. Practical implications Our findings demonstrate that for large-scale location searches, the GA with a local neighborhood search performs very well in comparison to CPLEX but at a fraction of the execution time. Originality/value Our findings provide insight into how to make improved decisions for the placement of deterrence and defense systems and the effectiveness of a hybrid metaheuristic in addressing associated computational challenges.

J.D. Lessan

Geoff Pond

A genetic algorithm-based solution for multi-type maximal covering location problem (MMCLP): application to defense and deterrence

基于遗传算法的多类型最大覆盖选址问题（MMCLP）解决方案：应用于防御与威慑

Purpose Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations. Design/methodology/approach This “flickering” behavior often results from CV algorithms treating successive observations as independent, which ignores the dependence inherent in most videos. Bayesian neural networks are a potential remedy to this issue using Bayesian priors. This research compares a traditional video classification neural network to its Bayesian equivalent based on performance and capabilities. Additionally, this work introduces the concept of smoothing to reduce the opportunities for “flickering.” Findings The augmentation of Bayesian layers to CNNs matched with an exponentially decaying weighted average for classifications demonstrates promising benefits in reducing flickering. In the best case the proposed Bayesian CNN model reduces flickering by 67% while maintaining both overall accuracy and class level accuracy. Research limitations/implications The training of the Bayesian CNN is more computationally demanding and the requirement to classify frames multiple times reduces resulting framerate. However, for some high surety mission applications this is a tradeoff the decision analyst may be willing to make. Originality/value Our research expands on previous efforts by first using a variable number of frames to produce the moving average as well as by using an exponentially decaying moving average in conjunction with Bayesian augmentation.

Noah Miller

Glen Ryan Drumm

Lance Champagne

Bruce Cox

Trevor Bihl

Strategies to alleviate flickering: Bayesian and smoothing methods for deep learning classification in video

缓解闪烁的策略：贝叶斯与平滑方法在视频深度学习分类中的应用

Purpose The rapid advancement of new technologies necessitates the adaptation of existing resources to address the evolving demands of contemporary conflicts among nations. As exemplified by the emergence of Unmanned Aerial Systems (UAS) and Unmanned Aerial Vehicles (UAVs), these technologies have become indispensable components of military drone operations. This study investigates the role of innovation models in driving Portuguese Innovation and Development, focusing specifically on military drones. Design/methodology/approach Through an examination of existing scientific and doctrinal literature, this research establishes a connection between theoretical concepts and the practical deployment of drones within the Portuguese ground forces. Employing a qualitative approach, this study follows a case study, with data collection following the principle of triangulation. Findings The findings of this research reveal several attributes crucial to drone utilization, such as optronic capability and drone range. These attributes are vital for ensuring high-quality, real-time imagery transmission and operational effectiveness in military activities. Originality/value This study contributes to the understanding of the implications of drone attributes for the Command, Control, Communications and Information Systems (C3IS) capabilities of the Portuguese ground forces. It highlights the importance of continued emphasis on drone development and innovation as a key priority for the Portuguese Army.

João Pedro Barros

João Reis

Nuno Melão

Adriane Cavalieri

Key features and applications of military drones: a case study from the Portuguese military ground forces

葡萄牙陆军地面部队军用无人机的主要特点与应用：一项案例研究

We are pleased to present this inaugural issue of the Journal of Defense Analytics and Logistics (JDAL).The JDAL will promote and foster discussion and proliferation of new theory and applications across the logistics, operations research, data analytics and supply chain management space, particularly as that space intersects with the defense community.One might ask why introduce this journal now with the plethora of publishing outlets that are already available.The answer is that the community still lacks an outlet for rigorously reviewed, high quality work focused on the unique analytical and logistical challenges faced by the defense community.To this end, we will bring together key research and practical applications, making the work available not only to the academic community but also to the defense community that this research supports.Defense analytics supports a wide range of theory and applications.The growth of operations research since the Second World War has been quite dramatic.Early works firmly established quantitative approaches based on mathematical programming and probabilistic modeling, as well as modeling and simulation and statistical modeling.Current "buzz words" (such as big data and machine learning) represent the new "toolisms" that will dominate defense analysis and discussion for some time.The JDAL welcomes work in both the old and the new techniques, particularly as those works help evolve the defense establishment toward the beneficial employment of these analytical techniques.Governments across the globe spend a combined US$1.76tn on defense.For instance, with an annual budget of nearly US$600bn, the US Department of Defense (DoD) represents the largest government expenditure in the world, representing 54 per cent of discretionary US spending in 2015.Indeed, the US DoD alone manages a US$309bn supply chain, and several of the largest public companies in the world support the global defense industry.Since the dawn of human conflict, logistics capabilities and supply chain management have been used to enable defense power projection.Modern supply chains incorporate information technology, resiliency and other technological and administrative innovations to meet the challenges of an ever-changing military-industrial complex.As the digitization of the supply chain becomes a reality, there is also a need to incorporate data science and analytical breakthroughs to provide greater supply chain insights and enhanced decisionmaking abilities.A focus on these latest technologies is encouraged.That said, the JDAL will be looking for cutting-edge theories and implementations in any area impacting the defense-industrial logistics complex.We developed the JDAL review process for regular research papers around a doubleblind review process; neither the authors nor the reviewers are made aware of the other's identity during the review process.This approach removes potential biases in the review process, thereby retaining quality and objectivity.At JDAL, we believe in a traditional peerreview process versus the convoluted and inefficient "peer-writing" process that seems to be used by many top journals today.We wish to report research findings based on the

Ben Hazen

Raymond R. Hill

Editorial: Welcome to the Journal of Defense Analytics and Logistics

编者按：欢迎阅读《国防分析与物流杂志》

Purpose The purpose of this paper is to demonstrate an improved method for forecasting the US Army recruiting. Design/methodology/approach Time series methods, regression modeling, principle components and marketing research are included in this paper. Findings This paper found the unique ability of multiple statistical methods applied to a forecasting context to consider the effects of inputs that are controlled to some degree by a decision maker. Research limitations/implications This work will successfully inform the US Army recruiting leadership on how this improved methodology will improve their recruitment process. Practical implications Improved US Army analytical technique for forecasting recruiting goals.. Originality/value This work culls data from open sources, using a zip-code-based classification method to develop more comprehensive forecasting methods with which US Army recruiting leaders can better establish recruiting goals.

Joshua L. McDonald

Edward D. White

Christian Pardo

Forecasting US Army enlistment contract production in complex geographical marketing areas

预测复杂地理营销区域中的美国陆军入伍合同生产

Purpose This paper examines the relationship between a prime contractor's financial health and its mergers and acquisitions (M&amp;A) spending in the defense industry. It aims to provide models that give the United States Department of Defense (DoD) indications of future M&amp;A activity, informing decision-makers and contributing to ensuring competitive markets that benefit the consumer. Design/methodology/approach The study uses panel data regression models on 40 companies between 1985 and 2021. The company's financial health is assessed using industry-standard financial ratios (i.e. measures of profitability, efficiency, solvency and liquidity) while controlling for economic factors such as national productivity, defense budgets and firm size. Findings The results show a significant relationship between efficiency and M&amp;A spending, indicating that companies with lower efficiency tend to spend more on M&amp;As. However, there was no significant relationship between M&amp;A spending and a company's profitability or solvency. These results were consistent with previous research and the study's hypotheses for profitability and solvency. However, the effect of liquidity was the opposite of the expected result, possibly due to the defense industry's different view on liquidity compared to previous research. Originality/value The paper provides insights into the relationship between a prime contractor's financial health and its M&amp;A spending, a topic with limited research. The findings can inform policymakers and regulators on the industrial base's future M&amp;A activity, ensuring competitive markets that benefit the consumer.

Corey Mack

Clay Koschnick

Michael Brown

Jonathan D. Ritschel

Brandon Lucas

A panel data regression model for defense merger and acquisition activity

防御并购活动的面板数据回归模型

Purpose In light of the recently experienced systemic shocks (the COVID-19 pandemic and the war in Ukraine), we investigate supply chain robustness. We aim to understand the potential consequences of uncertain events or adversary’s action on critical supplies in the Alliance. Design/methodology/approach We leverage a parsimonious supply chain model and investigate the relationship between upstream supplier concentration/diversification and the supply chain’s robustness (survival probability) in the presence of uncertain systemic shocks. In several scenarios of shock events, we simulate alternative input sourcing strategies in the presence of uncertainty. Findings A firm-level cost-focused optimisation may lead all upstream suppliers to concentrate in one location, which – when subsequently hit by a shock – would result in a disruption of the entire supply chain. A chain-level forward-looking optimisation diversifies the upstream supplier location and sourcing decisions. As a result, the supply chain’s survival probability is maximised, and critical supplies will continue even under the most demanding circumstances. Research limitations/implications Our findings encourage political and military decision makers to enhance upstream supply chain robustness in critical and strategic sectors, such as the diversification of nitrocellulose supplies currently sourced almost exclusively from China by European gunpowder manufacturers. Practical implications Our findings have direct recommendations to supply chain downstream decision makers and to the government’s policy choices. Since global supply chain (GSC) disruptions in critical sectors may have catastrophic impacts on social welfare and the probability of shocks such as COVID-19 and Russia’s war may not be known even approximately, robust decision rules seem to be the appropriate tools for policymaking in critical and strategic sectors such as energy supplies, food and water, communication and defence. A robust supply chain is one in which the survival probability is maximised, which we show in a central planner strategy’s simulations. Originality/value The paper shows formally why a market-based global input sourcing strategy may be efficient from an individual firm’s perspective but may be suboptimal from a societal resilience perspective.

d'Artis Kancs

Enhancing resilience: model-based simulations

增强韧性：基于模型的模拟

Publication Information	Publisher: Emerald Publishing，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2017，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Average review cycle	网友分享经验：12 Weeks

Journal of Defense Analytics and Logistics

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A genetic algorithm-based solution for multi-type maximal covering location problem (MMCLP): application to defense and deterrence

Strategies to alleviate flickering: Bayesian and smoothing methods for deep learning classification in video

Key features and applications of military drones: a case study from the Portuguese military ground forces

Editorial: Welcome to the Journal of Defense Analytics and Logistics

Forecasting US Army enlistment contract production in complex geographical marketing areas

A genetic algorithm-based solution for multi-type maximal covering location problem (MMCLP): application to defense and deterrence

Strategies to alleviate flickering: Bayesian and smoothing methods for deep learning classification in video

Key features and applications of military drones: a case study from the Portuguese military ground forces

A panel data regression model for defense merger and acquisition activity

Enhancing resilience: model-based simulations

ISSN	2399-6439, 2399-6447
h-index	4