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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.

Structural equation modeling (SEM) is a well-established and frequently applied method in various disciplines. New methods in the context of SEM are being introduced in an ongoing manner. Since formal proof of statistical properties is difficult or impossible, new methods are frequently justified using Monte Carlo simulations. For SEM with covariance-based estimators, several tools are available to perform Monte Carlo simulations. Moreover, several guidelines on how to conduct a Monte Carlo simulation for SEM with these tools have been introduced. In contrast, software to estimate structural equation models with variance-based estimators such as partial least squares path modeling (PLS-PM) is limited.

As a remedy, the R package cSEM which allows researchers to estimate structural equation models and to perform Monte Carlo simulations for SEM with variance-based estimators has been introduced. This manuscript provides guidelines on how to conduct a Monte Carlo simulation for SEM with variance-based estimators using the R packages cSEM and cSEM.DGP.

The author introduces and recommends a six-step procedure to be followed in conducting each Monte Carlo simulation.

For each of the steps, common design patterns are given. Moreover, these guidelines are illustrated by an example Monte Carlo simulation with ready-to-use R code showing that PLS-PM needs the constructs to be embedded in a nomological net to yield valuable results.

The purpose of this paper is to deal with the appropriateness of using the Monte Carlo simulation as a technique to calculate risk in real estate development.

The paper is divided into two interlinked segments. The first segment examines the general definition of risk and Monte Carlo simulation methodology as a tool to estimate risk. The second outlines the appropriateness of using Monte Carlo as a tool to model real estate development, given the lack of data quality and its inability to account for human relationships in the development process.

It is important that the Monte Carlo Simulation model is used as prescriptive model that builds on the original elicitation procedures; produces initial results; allows for detailed sensitivity analysis and then remodels as required. In short, to be fully effective, the Monte Carlo Simulation model needs to be used in a complementary fashion with an understanding of human judgement and decision making.

A fuller analysis may include an examination of the uncertainties in each of the components of the appraisal and account for the appropriate distributions of each of these variables. This is generally referred to as a Monte Carlo simulation. The argument in favour of a Monte Carlo simulation is that it helps the appraiser have a better understanding of the possible outcomes for the development and the relative impact of each input in the pricing of the project.

A lot of work has been done looking at scenario modelling with probabilities and the results that ensue. However, it is important that these quantitative results are placed in the context of the heuristic and cognitive approaches adopted by the decision maker. In other words, the behaviour of the decision maker is as influential in the interpretation of the results as the numbers themselves. This paper looks at the advantages and disadvantages of using Monte Carlo simulation in this context.

This study contributes significantly to the practical application of probability‐based models to development appraisal. The findings of the study are useful for users of feasibility studies to understand the context in which a development feasibility is carried out, and for appraisers to extend the scope of their analysis when carrying them out.

The purpose of this paper is to propose the idea of linking the use of the Monte Carlo simulation with scenario planning to assist strategy makers in formulating strategy in the face of uncertainty relating to service quality gaps for life insurance business, where discontinuities always remain for need‐based selling.

The paper reviews briefly some applications of scenario planning. Scenario planning emphasizes the development of a strategic plan that is robust across different scenarios. The paper provides considerable evidence to suggest a new strategic approach using Monte Carlo simulation for making scenario planning.

The paper highlights which particular service quality gap attribute as risk impacts most and least for the possibility of occurrences as best case, worst case, and most likely case.

This study suffers from methodological limitations associated with convenience sampling and anonymous survey‐based research.

The approach using Monte Carlo simulation increases the credibility of the scenario to an acceptable level, so that it will be used by managers and other decision makers.

The paper provides a thorough documentation on scenario planning upon studying the impact of risk and uncertainty in service quality gap for making rational decisions in management of services such that managers make better justification and communication for their arguments.

The paper offers empirical understanding of the application of Monte Carlo simulation to scenario planning and identifies key drivers which impact most and least on service quality gap.

This paper proposes a method to model and assess the availability and reliability of a system when numerous factors such as system complexity, wide range of failure modes, environment, and sustainability may influence system behaviour.

The approach for reliability/availability study is using continuous time stochastic simulation (Monte Carlo simulation) and is based on seven steps for covering logical phases from system description to simulation result discussion. The feasibility and benefits of this approach are shown in a case study on cogeneration plant.

Owing to the factors influencing the system behaviour, the opportunity to carry out system availability/reliability assessment through analytical models will be many times very restrictive. Thus a general approach to this problem is proposed based on Monte Carlo (stochastic) simulation. The simulation of the system's life process will be carried out in the computer, and estimates will be made for the desired measures of performance. The simulation will then be treated as a series of real experiments, and statistical inference will then be used to estimate confidence intervals for the performance metrics.

Individuals, companies as well as society in general are becoming more and more dependent on increasingly complex technical systems. Moreover, failure of these complex systems often causes a major loss of service with potentially serious consequences (i.e. critical risk). Thus their dependability with its facets such as reliability, availability, safety has become an important issue. For example, the ability of reliability/availability assessment of such systems is invaluable in industrial domains. Indeed reliability/availability assessment is used for various purposes such as maintenance strategy selection, maintenance planning, production planning, risk and cost evaluations. To face with this complexity, the existing analytical models are not well adapted to carry out system modelling and assessment due mainly to assumptions that are difficult to validate. This paper looks into this issue by proposing a generic approach based on Monte Carlo (stochastic) simulation.

The Monte Carlo simulation method allows one to consider various relevant aspects of systems operation that cannot be easily captured by analytical models. The utilisation of this method is growing for the assessment of overall plants availability and the monetary value of plant operation.

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.

In smaller projects with limited resources, schedule updates are often not performed. In these situations, traditional delay analysis methods cannot be used as they all require updated schedules. The objective of this study is to develop a model that performs delay analysis by using only an as-planned schedule and the expense records kept on site.

This study starts out by developing an approach that estimates activity duration ranges in a network schedule by using as-planned and as-built s-curves. Monte Carlo simulation is performed to generate candidate as-built schedules using these activity duration ranges. If necessary, the duration ranges are refined by a follow-up procedure that systematically relaxes the ranges and develops new as-built schedules. The candidate schedule that has the closest s-curve to the actual s-curve is considered to be the most realistic as-built schedule. Finally, the as-planned vs. as-built delay analysis method is performed to determine which activity(ies) caused project delay. This process is automated using Matlab. A test case is used to demonstrate that the proposed automated method can work well.

The automated process developed in this study has the capability to develop activity duration ranges, perform Monte Carlo simulation, generate a large number of candidate as-built schedules, build s-curves for each of the candidate schedules and identify the most realistic one that has an s-curve that is closest to the actual as-built s-curve. The test case confirmed that the proposed automated system works well as it resulted in an as-built schedule that has an s-curve that is identical to the actual as-built s-curve. To develop an as-built schedule using this method is a reasonable way to make a case in or out of a court of law.

Practitioners specifying activity ranges to perform Monte Carlo simulation can be characterized as subjective and perhaps arbitrary. To minimize the effects of this limitation, this study proposes a method that determines duration ranges by comparing as-built and as-planned cash-flows, and then by systematically modifying the search space. Another limitation is the assumption that the precedence logic in the as-planned network remains the same throughout construction. Since updated schedules are not available in the scenario considered in this study, and since in small projects the logic relationships are fairly stable over the short project duration, the assumption of a stable logic throughout construction may be reasonable, but this issue needs to be explored further in future research.

Delays are common in construction projects regardless of the size of the project. The critical path method (CPM) schedules of many smaller projects, especially in developing countries, are not updated during construction. In case updated schedules are not available, the method presented in this paper represents an automated, practical and easy-to-use tool that allows parties to a contract to perform delay analysis with only an as-planned schedule and the expense logs kept on site.

Since an as-built schedule cannot be built without updated schedules, and since the absence of an as-built schedule precludes the use of any delay analysis method that is acceptable in courts of law, using the method presented in this paper may very well be the only solution to the problem.

This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance.

The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables.

The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context.

This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain.

This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties.

This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance.

The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.

The purpose of this paper is to study the complex aerosol dynamic processes by using this newly developed stochastically weighted operator splitting Monte Carlo (SWOSMC) method.

Stochastically weighted particle method and operator splitting method are coupled to formulate the SWOSMC method for the numerical simulation of particle-fluid systems undergoing the complex simultaneous processes.

This SWOSMC method is first validated by comparing its numerical simulation results of constant rate coagulation and linear rate condensation with the corresponding analytical solutions. Coagulation and nucleation cases are further studied whose results are compared with the sectional method in excellent agreement. This SWOSMC method has also demonstrated its high numerical simulation capability when used to deal with simultaneous aerosol dynamic processes including coagulation, nucleation and condensation.

There always exists conflict and tradeoffs between computational cost and accuracy for Monte Carlo-based methods for the numerical simulation of aerosol dynamics. The operator splitting method has been widely used in solving complex partial differential equations, while the stochastic-weighted particle method has been commonly used in numerical simulation of aerosol dynamics. However, the integration of these two methods has not been well investigated.

The purpose of this paper is to present an integrated technical and economic model to evaluate the reusability of products or components.

Life cycle assessment (LCA) methodology is applied to obtain the product’s environmental performance. Monte Carlo simulation is utilized for enabling sustainable product design.

The results show that the model is capable of assessing the potential reusability of used products, while the usage of simulation significantly increases the effectiveness of the model in addressing uncertainties.

The case study has been conducted in a single manufacturing organization. The implications derived from the study are found to be practical and useful to the organization.

The paper reports a case study carried out for an Indian rotary switches manufacturing organization. Hence, the model is practically feasible.

The article presents a study that investigates LCA and simulation as enablers of sustainable product design. Hence, the contributions of this article are original and valuable.