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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.

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.

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.

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

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.

The purpose of this paper is to investigate the benefit of pre-emptive disruption management measures for assembly systems towards the target dimension adherence to delivery times.

The research was conducted by creating simulation models for typical assembly systems and measuring its varying throughput times due to changes in their disruption profiles. Due to the variability of assembly systems, key influence factors were investigated and used as a foundation for the simulation setup. Additionally, a disruption profile for each simulated process was developed, using the established disruption categories material, information and capacity. The categories are described by statistical distributions, defining the interval between the disruptions and the disruption duration. By a statistical experiment plan, the effect of a reduced disruption potential onto the throughput time was investigated.

Pre-emptive disruption management is beneficial, but its benefit depends on the operated assembly system and its organisation form, such as line or group assembly. Measures have on average a higher beneficial impact on group assemblies than on line assemblies. Furthermore, it was proven that the benefit, in form of better adherence to delivery times, per reduced disruption potential has a declining character and approximates a distinct maximum.

Characterising the benefit of pre-emptive disruption management measures enables managers to use this concept in their daily production to minimise overall costs. Despite the hardly predictable influence of pre-emptive disruption measures, these research results can be implemented into a heuristic for efficiently choosing these measures.

Many mathematical models have been shared to communicate about the COVID-19 outbreak; however, they require advanced mathematical skills. The main purpose of this study is to investigate in which way computational thinking (CT) tools and concepts are helpful to better understand the outbreak, and how the context of disease could be used as a real-world context to promote elementary and middle-grade students' mathematical and computational knowledge and skills.

In this study, the authors used a qualitative research design, specifically content analysis, and analyzed two simulations of basic SIR models designed in a Scratch. The authors examine the extent to which they help with the understanding of the parameters, rates and the effect of variations in control measures in the mathematical models.

This paper investigated the four dimensions of sample simulations: initialization, movements, transmission, recovery process and their connections to school mathematical and computational concepts.

A major limitation is that this study took place during the pandemic and the authors could not collect empirical data.

Teaching mathematical modeling and computer programming is enhanced by elaborating in a specific context. This may serve as a springboard for encouraging students to engage in real-world problems and to promote using their knowledge and skills in making well-informed decisions in future crises.

This research not only sheds light on the way of helping students respond to the challenges of the outbreak but also explores the opportunities it offers to motivate students by showing the value and relevance of CT and mathematics (Albrecht and Karabenick, 2018).