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  Subjects -> MILITARY (Total: 106 journals)
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Journal of Defense Analytics and Logistics
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2399-6439 - ISSN (Online) 2399-6447
Published by Emerald Homepage  [362 journals]
  • Influencing air force logisticians' information seeking during the
           COVID-19 pandemic: the role of organizational meetings in an expanded
           PRISM framework

    • Authors: Matthew D. Roberts, Christopher T. Price, Seong-Jong Joo
      Abstract: This research aims to understand how organizational workplace meetings surrounding the COVID-19 pandemic impacted logistics Airmen across the United States Air Force and how these meetings impacted their risk seeking behavior on social media. This survey research tested an extended Planned Risk Information Risk Seeking Model (PRISM) with organizational meetings as an antecedent to determine if current meetings influenced an Airman's perceived behavioral control, attitude toward seeking, subjective norms, knowledge sufficiency and intention to seek information regarding COVID-19. Results of the CFA showed that the expanded PRISM model had good model fit. Additionally, using a custom dialog PROCESS macro in SPSS, it was found that perceptions of existing meetings were directly, positively related to attitude toward seeking, subjective norms and perceived behavioral control, and indirectly related to knowledge sufficiency threshold and information seeking. Theoretical and managerial implications are discussed. This research adds to the limited body of knowledge of crisis communication and effectively expands the PRISM model to include an antecedent that helps explain information seeking during times of uncertainty.
      Citation: Journal of Defense Analytics and Logistics
      PubDate: 2023-09-19
      DOI: 10.1108/JDAL-03-2023-0002
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
  • A hierarchical cluster approach¬†toward understanding the regional
           variable in country conflict modeling

    • Authors: Benjamin Leiby, Darryl Ahner
      Abstract: This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions. This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used. This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust. This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.
      Citation: Journal of Defense Analytics and Logistics
      PubDate: 2023-05-30
      DOI: 10.1108/JDAL-11-2022-0011
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
  • US Army Aviation air movement operations assignment, utilization and

    • Authors: Russell Nelson, Russell King, Brandon M. McConnell, Kristin Thoney-Barletta
      Abstract: The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost. In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period. The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time. Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight. This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits.
      Citation: Journal of Defense Analytics and Logistics
      PubDate: 2023-05-23
      DOI: 10.1108/JDAL-11-2022-0013
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
  • Small arms combat modeling: a superior way to evaluate marksmanship

    • Authors: Adam Biggs, Greg Huffman, Joseph Hamilton, Ken Javes, Jacob Brookfield, Anthony Viggiani, John Costa, Rachel R. Markwald
      Abstract: Marksmanship data is a staple of military and law enforcement evaluations. This ubiquitous nature creates a critical need to use all relevant information and to convey outcomes in a meaningful way for the end users. The purpose of this study is to demonstrate how simple simulation techniques can improve interpretations of marksmanship data. This study uses three simulations to demonstrate the advantages of small arms combat modeling, including (1) the benefits of incorporating a Markov Chain into Monte Carlo shooting simulations; (2) how small arms combat modeling is superior to point-based evaluations; and (3) why continuous-time chains better capture performance than discrete-time chains. The proposed method reduces ambiguity in low-accuracy scenarios while also incorporating a more holistic view of performance as outcomes simultaneously incorporate speed and accuracy rather than holding one constant. This process determines the probability of winning an engagement against a given opponent while circumventing arbitrary discussions of speed and accuracy trade-offs. Someone wins 70% of combat engagements against a given opponent rather than scoring 15 more points. Moreover, risk exposure is quantified by determining the likely casualties suffered to achieve victory. This combination makes the practical consequences of human performance differences tangible to the end users. Taken together, this approach advances the operations research analyses of squad-level combat engagements. For more than a century, marksmanship evaluations have used point-based systems to classify shooters. However, these scoring methods were developed for competitive integrity rather than lethality as points do not adequately capture combat capabilities. The proposed method thus represents a major shift in the marksmanship scoring paradigm.
      Citation: Journal of Defense Analytics and Logistics
      PubDate: 2023-05-18
      DOI: 10.1108/JDAL-11-2022-0012
      Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
  • Systems literacy amongst air force logisticians in Nigeria

    • Authors: Usman Umar Akeel
      Abstract: The purpose of this research is to assess the current level of systems literacy of air force logisticians in Nigeria. This research undertook an assessment of the knowledge of air force logistics officers on systems thinking with the aid of a qualitative questionnaire. The questionnaire featured questions on the level of literacy and application of systems thinking by air force logisticians in Nigeria. The research finds that the majority of the air force logistics officers have very low levels of knowledge and training in systems thinking. The research is a unique effort to ascertain the level of systems thinking literacy and training in air force logistics in Nigeria. The study presents a baseline and justification for intervention through an improvement of the logistics curricula used in air force training institutions in Nigeria.
      Citation: Journal of Defense Analytics and Logistics
      PubDate: 2022-11-30
      DOI: 10.1108/JDAL-10-2022-0009
      Issue No: Vol. 6, No. 2 (2022)
  • Determining the best ship loading strategy during military deployments

    • Authors: Dave C. Longhorn, Shelby V. Baybordi, Joel T. Van Dyke, Austin W. Winter, Christopher L. Jakes
      Abstract: This study aims to examine ship loading strategies during large-scale military deployments. Ships are usually loaded to a stowage goal of about 65% of the ship's capacity. The authors identify how much cargo to load onto ships for each sailing and propose lower stowage goals that could improve the delivery of forces during the deployment. The authors construct several mixed integer programs to identify optimal ship loading strategies that minimize delivery timelines for notional, but realistic, problem variables. The authors study the relative importance of these variables using experimental designs, regressions, correlations and chi-square tests of the empirical results. The research specifies the conditions during which ships should be light loaded, i.e. loaded to less than 65% of total capacity. Empirical results show cargo delivered up to 16% faster with a light-loaded strategy compared to fully loaded ships. This work assumes deterministic sailing times and ship loading times. Also, all timing aspects of the problem are estimated to the nearest natural number of days. This research provides important new insights about optimal ship loading strategies, which were not previously quantified. More importantly, logistics planners could use these insights to reduce sealift delivery timelines during military deployments. Most ship routing and scheduling problems minimize costs as the primary goal. This research identifies the situations in which ships transporting military forces should be light loaded, thereby trading efficiency for effectiveness, to enable faster overall delivery of unit equipment to theater seaports.
      Citation: Journal of Defense Analytics and Logistics
      PubDate: 2022-11-22
      DOI: 10.1108/JDAL-07-2022-0003
      Issue No: Vol. 6, No. 2 (2022)
  • Contested logistics simulation output analysis with approximate dynamic
           programming: a¬†proposed methodology

    • Authors: Matthew Powers, Brian O'Flynn
      Abstract: Rapid sensitivity analysis and near-optimal decision-making in contested environments are valuable requirements when providing military logistics support. Port of debarkation denial motivates maneuver from strategic operational locations, further complicating logistics support. Simulations enable rapid concept design, experiment and testing that meet these complicated logistic support demands. However, simulation model analyses are time consuming as output data complexity grows with simulation input. This paper proposes a methodology that leverages the benefits of simulation-based insight and the computational speed of approximate dynamic programming (ADP). This paper describes a simulated contested logistics environment and demonstrates how output data informs the parameters required for the ADP dialect of reinforcement learning (aka Q-learning). Q-learning output includes a near-optimal policy that prescribes decisions for each state modeled in the simulation. This paper's methods conform to DoD simulation modeling practices complemented with AI-enabled decision-making. This study demonstrates simulation output data as a means of state–space reduction to mitigate the curse of dimensionality. Furthermore, massive amounts of simulation output data become unwieldy. This work demonstrates how Q-learning parameters reflect simulation inputs so that simulation model behavior can compare to near-optimal policies. Fast computation is attractive for sensitivity analysis while divorcing evaluation from scenario-based limitations. The United States military is eager to embrace emerging AI analytic techniques to inform decision-making but is hesitant to abandon simulation modeling. This paper proposes Q-learning as an aid to overcome cognitive limitations in a way that satisfies the desire to wield AI-enabled decision-making combined with modeling and simulation.
      Citation: Journal of Defense Analytics and Logistics
      PubDate: 2022-11-15
      DOI: 10.1108/JDAL-07-2022-0004
      Issue No: Vol. 6, No. 2 (2022)
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