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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.

The purpose of this paper is to propose a bearing replacement strategy which employs the Monte Carlo simulation method. In this contribution the method is used to estimate the economic impact on the selection of a particular bearing change strategy. The simulation demonstrates that it is possible to identify the most cost‐effective approach and thus suggests a suitable bearing replacement policy, which in turn allows engineers to develop the appropriate maintenance schedules for their company.

The paper develops the Monte Carlo method through a case study approach. Three case studies are presented. The first study is detailed and chronicles the design, development and implementation of the Monte Carlo method as a means of defining a bearing replacement strategy within a subject company. The second and third cases outline the application of the Monte Carlo method in two different environments. These applications made it possible to obtain proof of concept and also to further prove the effectiveness of the Monte Carlo simulation approach.

An effective development of the Monte Carlo approach is proposed and the effectiveness of the method is subsequently evaluated, highlighting the benefits to the host organization and how the approach led to significant improvement in machinery reliability through a bearing replacement strategy.

The design, development and implementation of a bearing replacement strategy provide a simple yet effective approach to achieving significant improvements in system reliability and performance through less downtime and greater cost savings. The paper offers practising maintenance managers and engineers a strategic framework for increasing productive efficiency and output.

The proposed bearing replacement strategy contributes to the existing knowledge base on maintenance systems and subsequently disseminates this information in order to provide impetus, guidance and support towards increasing the development companies in an attempt to move the UK manufacturing sector towards world‐class manufacturing performance.

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.

In this paper we use Monte Carlo sampling experiments to examine the properties of pretest estimators in the random parameters logit (RPL) model. The pretests are for the presence of random parameters. We study the Lagrange multiplier (LM), likelihood ratio (LR), and Wald tests, using conditional logit as the restricted model. The LM test is the fastest test to implement among these three test procedures since it only uses restricted, conditional logit, estimates. However, the LM-based pretest estimator has poor risk properties. The ratio of LM-based pretest estimator root mean squared error (RMSE) to the random parameters logit model estimator RMSE diverges from one with increases in the standard deviation of the parameter distribution. The LR and Wald tests exhibit properties of consistent tests, with the power approaching one as the specification error increases, so that the pretest estimator is consistent. We explore the power of these three tests for the random parameters by calculating the empirical percentile values, size, and rejection rates of the test statistics. We find the power of LR and Wald tests decreases with increases in the mean of the coefficient distribution. The LM test has the weakest power for presence of the random coefficient in the RPL model.

The Monte‐Carlo particle model is a technique of simulating small semiconductor devices. It consists briefly of following the detailed transport histories of individual carriers, their time of free flight and consequent scattering chosen by a random number technique. A description of the method is given. The method has proved itself successful in semiconductor analysis, and as an example of its application we are using it to study the influence the epitaxial doping has on the performance of field‐effect transistors. We are comparing a transistor with an epitaxially grown active layer, with one with an ion implanted active layer and with an ideal device with an abrupt transition between the epilayer and the substrate. The cut‐off bias for ideal transistor is found to be more sharply defined than for the other two types of transistors. The spatial distribution of the carriers follows roughly the doping profile near the source. Underneath the gate the peak of the carrier density is pushed further down and into the substrate as the gate bias increases. This peak also weakens as the gate bias rises, and vanishes at, and beyond cut‐off. In the high field region after the gate the upper valleys population increases with increased drain bias and decreases with increased gate bias. The power gain and the y‐parameters are examined for all devices, both near pinch‐off and for no external gate bias. In both cases the ion implanted transistor shows the greatest gain. This transistor also exhibits the lowest minimum noise figure.

We present a generalized self‐scattering method for generating carrier free flight times in Monte Carlo simulation. Compared to traditional approaches, the added flexibility of this approach results in fewer fictitious scatterings, which is especially appealing for load balance and efficiency when a SIMD parallel computer is used. Speedups from 19% to 69% over an optimized variable‐Γ approach are shown for an implementation on the Connection Machine CM‐2. The performance sensitivities to applied fields and grid spacings are also presented. The conversion of existing variable‐Γ software to this new approach requires only a few changes.

Uncertainty is ubiquitous in practical engineering and scientific research. The uncertainties in parameters can be treated as interval numbers. The prediction of upper and lower bounds of the response of a system including uncertain parameters is of immense significance in uncertainty analysis. This paper aims to evaluate the upper and lower bounds of electric potentials in an electrostatic system efficiently with interval parameters.

The Taylor series expansion is proposed for evaluating the upper and lower bounds of electric potentials in an electrostatic system with interval parameters. The uncertain parameters of the electrostatic system are represented by interval notations. By performing Taylor series expansion on the electric potentials obtained using the equilibrium governing equation and by using the properties of interval mathematics, the upper and lower bounds of the electric potentials of an electrostatic system can be calculated.

To evaluate the accuracy and efficiency of the proposed method, the upper and lower bounds of the electric potentials and the computation time of the proposed method are compared with those obtained using the Monte Carlo simulation, which is referred to as a reference solution. Numerical examples illustrate that the bounds of electric potentials of this method are consistent with those obtained using the Monte Carlo simulation. Moreover, the proposed method is significantly more time-saving.

This paper provides a rapid computational method to estimate the upper and lower bounds of electric potentials in electrostatics analysis with interval parameters. The precision of the proposed method is acceptable for engineering applications, and the computation time of the proposed method is significantly less than that of the Monte Carlo simulation, which is the most widely used method related to uncertainties. The Monte Carlo simulation requires a large number of samplings, and this leads to significant runtime consumption.

This paper studies the pricing of collateralized debt obligation (CDO) using Monte Carlo and analytic methods. Both methods are developed within the framework of the reduced form model. One-factor Gaussian Copula is used for treating default correlations amongst the collateral portfolio. Based on the two methods, the portfolio loss, the expected loss in each CDO tranche, tranche spread, and the default delta sensitivity are analyzed with respect to different parameters such as maturity, default correlation, default intensity or hazard rate, and recovery rate. We provide a careful study of the effects of different parametric impact. Our results show that Monte Carlo method is slow and not robust in the calculation of default delta sensitivity. The analytic approach has comparative advantages for pricing CDO. We also employ empirical data to investigate the implied default correlation and base correlation of the CDO. The implication of extending the analytical approach to incorporating Levy processes is also discussed.

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.

Automakers are engaged in manufacturing both efficient and inefficient green cars. The purpose of this paper is to categorize efficient green cars and inefficient green cars followed by improving efficiencies of identified inefficient green cars for distribution fitting.

The authors have used 2014 edition of secondary data published by the Automotive Research Centre of the Automobile Club of Southern California. The paper provides the methodology of applying data envelopment analysis (DEA) consisting of 50 decision-making units (DMUs) of green cars with six input indices (emission, braking, ride quality, acceleration, turning circle, and luggage capacity) and two output indices (miles per gallon and torque) integrated with Monte Carlo simulation for drawing significant statistical inferences graphically.

The findings of this study showed that there are 27 efficient and 23 inefficient DMUs along with improvement matrix. Additionally, the study highlighted the best distribution fitting of improved efficient green cars for respective indices.

This study suffers from limitations associated with 2014 edition of secondary data used in this research.

This study may be useful for motorists with efficient listing of green cars, whereas automakers can be benefitted with distribution fitting of improved efficient green cars using Monte Carlo simulation for calibration.

The paper uses DEA to empirically examine classification of green cars and applies Monte Carlo simulation for distribution fitting to improved efficient green cars to decide appropriate range of their attributes for calibration.