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Suggests that many quantitative methods have been developed in an attempt to acknowledge uncertainty in decision making and to manage the inherent risk during economic planning for new technology implementation. Outlines a relatively simple and practical risk analysis, accompanied by the implementation of advanced manufacturing technology. Employs the proven analytical hierarchy process (AHP) approach, coupled with confidence intervals from data gathered through simulation techniques or through observations and empirical sampling. Describes a case study in which simulation models were developed using a software package SIMFACTORY II.5, which is based on the concept of visual interactive simulation. A number of simulation experiments were performed in order to investigate the influence of various flexible manufacturing system and cellular manufacturing system configurations. An AHP multi‐attribute analysis is performed by using AUTOMAN, a decision support software package. This package evaluates and combines the qualitative and quantitative factors for different configuration designs.

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.

Business process (BP) and information systems (IS) communities support the idea that BP and IS design should be integrated. Although there are a large number of modelling techniques to aid BP and IS design, there is little indication of which techniques can be suitable to model their integration. This research suggests a simulation model that can be used to depict BP/IS integration.

The simulation framework proposed in this paper is based on a simulation framework used previously. For both frameworks, a single case study approach is employed for theory building and testing. The results provided by the application of the ASSESS‐IT framework (theory testing) are used to propose the new ISBPS framework (theory building), which is tested again in the case study.

The models derived from the ISBPS framework provide quantifiable metrics of the integration of BP and IS. These data can help analysts to foresee the benefits that the insertion of a given IS design may bring to the organisational processes.

The development of the ISBPS models proved to be complex. Further research should focus on testing the ISBPS framework with more complex IS and to provide mechanisms to facilitate their design.

IS practitioners may consider simulation to evaluate IS design strategies.

The ISBPS simulation model provides quantifiable metrics of the integration of a given IS design within a given BP scenario. Practitioners can use these models to test alternative BP/IS scenarios before implementation.

Although logistics research contains numerous applications of computer simulation modelling, logistics literature has outlined no standard procedure for determining the appropriate sample size when employing simulation methodology in logistics research. Addresses this deficiency by considering the issue of sample size determination in logistics simulation models. Describes a technique for determining the sample size required to achieve the desired level of relative precision in logistics simulation models. In addition, provides an example of the application of this sample size determination technique to a logistics simulation.

One of the most challenging problems facing industrial engineers concerns the design and operational planning of today′s sophisticated production systems. The need for a detailed quantitative analysis is far more apparent than ever before. The application of discrete‐event simulation has been growing rapidly in the analysis of production systems. This is because no other quantitative methods can provide the flexibility, realism and predictive accuracy offered by the simulation technique. Although the important role that simulation can play in analysing production systems has now been generally realised, its use is not necessarily straightforward. The successful implementation of simulation projects usually depends on several factors which include, inter alia, the availability of simulation expertise and the ability of the available simulation software to model readily and accurately the environment under consideration. The areas of production systems where simulation can be applied are outlined. The essential considerations which must be studied when applying simulation are also discussed. An overview of simulation modelling environments that are currently used is then taken. Recommendations for future work of importance from the system analysis viewpoint are highlighted.

This research seeks to evaluate the impact of applying lean construction principles on the performance of reinforcement operations using a discrete-event simulation (DES) approach.

Process mapping of reinforcements operations was first established through field observation and interviews with construction managers involved in the selected project. Subsequently, quantitative data were gathered and then used to identify the best probabilistic density functions for each activity duration based on the fit-quality tests. Upon testing the validity of the real-world model, a lean simulation model was developed, using ARENA software, to investigate the impact of lean construction principles on the performance of such processes.

Lean principles are effective in enhancing the performance of the selected construction process. Output performance measurements for real-world model and lean model revealed that lean construction principles led to 41% improvement in process productivity, 14% enhancement in process efficiency and 17% reduction in cycle time.

The statistical findings only represent the process under study (reinforcement process) and cannot be generalized to other construction activities. In order to draw generalizable conclusions, future works are needed to extend this study to different project sizes and more complex construction processes (e.g. bricklaying process and concrete pouring operations). Moreover, there are other factors such as labor skills, rework and uncertainty, site conditions that require further analyses for leaner construction projects.

The methodology and the techniques presented in this work can be used for decision making by analyzing various lean construction scenarios and evaluating their impacts on performance outcomes of any construction process prior to real-world implementation.

This paper presents a case study of the use of business‐process simulation within the context of a business‐process‐reengineering approach to change. The process‐based change methodology provides context to the simulation technique in that it connects the aims of a business‐process simulation (BPS) study to the strategic aims of the organisation and incorporates a consideration of human factors in order to achieve successful implementation of redesigned processes. Conversely, the ability of BPS to incorporate system variability, scenario analysis and a visual display to communicate process performance makes it a useful technique to provide a realistic assessment of the need for, and results of, change.

The purpose of this paper is to conduct a survey on research in fabric and cloth simulation using mass spring model. Also in this paper some of the common methods in process of fabric simulation in mass spring model are discussed and compared.

This paper reviews and compares presented mesh types in mass spring model, forces applied on model, super elastic effect and ways to settle the super elasticity problem, numerical integration methods for solving equations, collision detection and its response. Some of common methods in fabric simulation are compared to each other. And by using examples of fabric simulation, advantages and limitations of each technique are mentioned.

Mass spring method is a fast and flexible technique with high ability to simulate fabric behavior in real time with different environmental conditions. Mass spring model has more accuracy than geometrical models and also it is faster than other physical modeling.

In the edge of digital, fabric simulation technology has been considered into many fields. 3D fabric simulation is complex and its implementation requires knowledge in different fields such as textile engineering, computer engineering and mechanical engineering. Several methods have been presented for fabric simulation such as physical and geometrical models. Mass spring model, the typical physically based method, is one of the methods for fabric simulation which widely considered by researchers.

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.