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Evaluating warfighter lethality is a critical aspect of military performance. Raw metrics such as marksmanship speed and accuracy can provide some insight, yet interpreting subtle differences can be challenging. For example, is a speed difference of 300 milliseconds more important than a 10% accuracy difference on the same drill? Marksmanship evaluations must have objective methods to differentiate between critical factors while maintaining a holistic view of human performance.

Monte Carlo simulations are one method to circumvent speed/accuracy trade-offs within marksmanship evaluations. They can accommodate both speed and accuracy implications simultaneously without needing to hold one constant for the sake of the other. Moreover, Monte Carlo simulations can incorporate variability as a key element of performance. This approach thus allows analysts to determine consistency of performance expectations when projecting future outcomes.

The review divides outcomes into both theoretical overview and practical implication sections. Each aspect of the Monte Carlo simulation can be addressed separately, reviewed and then incorporated as a potential component of small arms combat modeling. This application allows for new human performance practitioners to more quickly adopt the method for different applications.

Performance implications are often presented as inferential statistics. By using the Monte Carlo simulations, practitioners can present outcomes in terms of lethality. This method should help convey the impact of any marksmanship evaluation to senior leadership better than current inferential statistics, such as effect size measures.

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.

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.

The paper aims to estimates the limitations of a forced distribution performance appraisal system in identifying the highest performing individuals within an organization. Traditionally, manpower modeling allows organizations to develop plans that meet future human resource requirements by modeling the flow of personnel within an organization. The aim is to quantify the limitations of a performance appraisal system in identifying the best-qualified individuals to fill future requirements.

This paper describes an exploratory study using discrete event simulation based on the assignment, evaluation and promotion history of over 2,500 officers in the US Army. The obtained data provide a basis for estimating simulation inputs that include system structure, system dynamics, human behavior and policy constraints. The simulation approach facilitates modeling officers who receive evaluations as they move throughout the system over time.

The paper provides insights into the effect of system structure and system dynamics on the evaluation outcome of employees. It suggests that decreasing the number of a rater’s subordinates has a significant effect on the accuracy of performance appraisals. However, increasing the amount of time individuals spend on each assignment has little effect on system accuracy.

This research allows an organization’s leadership to evaluate the possible consequences associated with evaluation policy prior to policy implementation.

This work advances a framework in assessing the effect of system dynamics and structure, and the extent to which they limit or enhance the accuracy of an organization’s forced distribution performance appraisal system.

The purpose of this research is to explore the utility of autonomous transport across two independent airframe maintenance operations at a single location.

This study leveraged discrete event simulation that encompassed real-world conditions on a United States Air Force flight line. Though the Theory of Constraints (TOC) lens, a high-demand, human-controlled delivery asset is analyzed and the impact of introducing an autonomous rover delivery vehicle is assessed. The authors’ simulations explored varying numbers and networks of rovers as alternative sources of delivery and evaluated these resources’ impact against current flight line operations.

This research indicates that the addition of five autonomous rovers can significantly reduce daily expediter delivery tasks, which results in additional expertise necessary to manage and execute flight line operations. The authors assert that this relief would translate into enhancements in aircraft mission capable rates, which could increase overall transport capacity and cascade into faster cargo delivery times, systemwide. By extension, the authors suggest overall inventory management could be improved through reduction in transportation shipping time variance, which enhances the Department of Defense’s overall supply chain resilience posture.

When compared against existing practices, this novel research provides insight into actual flight line movement and the potential benefits of an alternative autonomous delivery system. Additionally, the research measures the potential savings in the workforce and vehicle use that exceeds the cost of the rovers and their employment.

An integrated queueing network focused on container storage/retrieval operations occurring on the yard of a transshipment hub is proposed. The purpose of the network is to support decisions related to the organization of the yard area, while also accounting for operations policies and times on the quay.

A discrete-event simulation model is used to reproduce container handling on both the quay and yard areas, along with the transfer operations between the two. The resulting times, properly estimated by the simulation output, are fed to a simpler queueing network amenable to solution via algorithms based on mean value analysis (MVA) for product-form networks.

Numerical results justify the proposed approach for getting a fast, yet accurate analytical solution that allows carrying out performance evaluation with respect to both organizational policies and operations management on the yard area.

Practically, the expected performance measures on the yard subsystem can be obtained avoiding additional time-expensive simulation experiments on the entire detailed model.

As a major takeaway, deepening the MVA for generally distributed service times has proven to produce reliable estimations on expected values for both user- and system-oriented performance metrics.

Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.

This paper aims to explore virtual simulations, merging artificial intelligence with real-world simulations, supporting Canadian armed forces (CAF) junior military leaders (JMLs) leadership development. Our research questions are: (1) How do virtual simulations support CAF junior military leadership development within a globalized and complex environment in the 21st century? (2) Could virtual simulations support a leadership culture change through efficacious “soft skills” training? In this paper, we explore the efficacy of virtual simulations for enhancing or developing leadership in JMLs in the CAF through a four-day pilot project with twenty JMLs (n = 20).

To assess the efficacy of virtual simulations for leadership development, we designed and studied a four-day leadership workshop for JMLs in the CAF using several virtual artificial intelligence leadership role-play simulations developed by McGraw Hill in their smart book textbook (Manning & Curtis, 2022) and several non-virtual in-class simulations for comparison. We selected four twenty to thirty-minute virtual role-play simulations that synergized with the in-person morning leadership workshop. We facilitated the three-hour leadership workshops and virtual/in-class simulations over four consecutive days. We emulated the ELESS model (De Freitas & Routledge, 2013) to assess soft and leadership skills.

The participants (JMLs) reported beneficial learning utility associated with the virtual simulations. Participants also expressed that further utility might be leveraged through virtual simulations incorporating greater complexity with multiple potential outcomes. They also suggested that leadership simulations designed around military situations would prove highly beneficial, something that was outside of the scope of this small pilot project.

Since this phase of our research is a pilot project, we secured a small amount of funding to test our hypothesis that simulations enhance leadership development for JMLs. These funding limitations resulted in several constraints in the research, such as the availability of virtual simulations articulating leadership from a military perspective. However, we believed the assigned organizational leadership simulations in the McGraw Hill Smart Book ecosystem would approximate generic leadership situations enough to test the hypothesis with the JMLs. As a pilot project, our sample size was relatively small (n = 20 JMLs) since participation was voluntary amidst a busy spring season for the JMLs. Since this is a pilot project, we suggest that twenty JMLs are an adequate sampling to test the hypothesis that simulations enhance JML leadership development. We will expand the sample size in the next phase of our research as we work with the CAF to expand the pool of participants to at least forty JML participants (n = 40). We also plan to secure further funding to collaborate with subject matter experts to design virtual simulations based on Canadian military leadership scenarios.

The CAF host robust simulations capabilities for combat training, but have not exploited the potential training and analytical capacity of virtual leadership simulations for leadership development within the CAF. We believe that virtual simulations provide an opportunity for the CAF to effect desired culture change through leadership development that leverages the substantial pedagogical benefits of simulations.

The CAF encountered several detrimental leadership scandals that eroded the reputational capital of the CAF. In the current geo-political climate of an expanding North American Treaty Organization (NATO) and threats from several international actors, the CAF seeks to expand its capabilities by adding and enhancing its human capital. However, the CAF currently experiences a significant gap in its human capital aspirations. There is a unanimous consensus that the endemic traditional culture of the CAF, as expressed in the recent explosive leadership scandals, is a deterrent to recruitment and thus weakens the CAF’s capability. The CAF targets leadership development with new leadership paradigms as pivotal to culture change. The CAF suggests that by enhancing leadership development in the CAF the new cadre of leadership will change the culture of the CAF and thereby enhance the reputational capital of the CAF. It is believed that this rejuvenated culture will lead to greater recruitment and retention, leading to a strengthened military. A strengthened military is important to provide effective support and protection for the Canadian people in these volatile and uncertain times. This expanded capacity will enable the CAF to address external military threats more effectively and also the increasing operations other than war (OOTW), such as the military support of long-term care facilities during COVID-19 or the military’s support in fighting record wildfires and the military’s support in climate change related disasters such as flooding.

The satisfaction measures indicated by the participants are typical evaluative measures of leadership development (Noe, 2023). These satisfaction ratings do not, however, indicate whether training has produced a change in behaviour (Brown, 2022). The implications of these outcomes for leadership education are that role-player simulations are useful leadership education and development tools because they provide a theatre of practice in which mistakes are not detrimental and serve as learning moments (Moore, 2012; Piro and O’Callaghan, 2021; Riotto, 2021). Further, the importance of role-player simulations that closely approximate the sector where leadership is experienced and practiced is perceived to enhance the experience. While the CAF invest in combat related simulations, but leadership development simulations are not as evident in the training and development array. This study seeks to assess their potential value as a leadership development tool within the wider context of character development as a leadership competency.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This research analyses challenges faced by users at various levels in planning and designing participatory simulation models of cities. It aims to identify issues that hinder experts from maximising the effectiveness of the SUMO tool. Additionally, evaluating current methods highlights their strengths and weaknesses, facilitating the use of participatory simulation advantages to address these issues. Finally, the presented case studies illustrate the diversity of user groups and emphasise the need for further development of blueprints.

In this research, action research was used to assess and improve a step-by-step guideline. The guideline's conceptual design is based on stakeholder analysis results from those involved in developing urban logistics scenarios and feedback from potential users. A two-round process of application and refinement was conducted to evaluate and enhance the guideline's initial version.

The guidelines still demand an advanced skill level in simulation modelling, rendering them less effective for the intended audience. However, they have proven beneficial in a simulation course for students, emphasising the importance of developing accurate conceptual models and the need for careful implementation.

This paper introduces a step-by-step guideline designed to tackle challenges in modelling urban logistics scenarios using SUMO simulation software. The guideline's effectiveness was tested and enhanced through experiments involving diverse groups of students, varying in their experience with simulation modelling. This approach demonstrates the guideline's applicability and adaptability across different skill levels.