A Comprehensive Review of Methods for Simulation Output Analysis

Alexopoulos,

doi:10.1109/wsc.2006.323070

Cited by 18 publications

(15 citation statements)

References 49 publications

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“…In a related area, Kleijnen (2005) reviews approaches to sensitivity analysis and designs for experiments in simulation modelling. Alexopoulos (2006) presents a comprehensive review of methods for the analysis of outputs from simulation experimentation. Müller and Schumann (2003) investigate methods for the visualization of time-dependent data that can be used to investigate aspects of simulation modelling and give an overview of dynamic presentation techniques and event-based visualization.…”

Section: Studies Of Simulation Modelling Literaturementioning

confidence: 99%

Simulation modelling is 50! Do we need a reality check?

Taylor

Eldabi

Riley

et al. 2009

Journal of the Operational Research Society

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Simulation modelling is a fascinating research field. The techniques and tools of simulation modelling have been used to research and investigate the behaviour of various systems in a wide range of areas such as commerce, computer networks, defence, health, manufacturing and transportation. Indeed, the study of the use of these techniques and tools, and the development of new forms of these, are a rich source of research in their own right. Simulation modelling is about to reach the 50th anniversary of the development of GSP (General Simulation Program), the first simulation modelling language (Tocher and Owen, 1960). There have been several historical accounts of simulation modelling research. To complement these, we have performed a review of the recent history of simulation modelling. This study targeted three leading journals dedicated to this field. These are the ACM Transactions of Modeling and Computer Simulation, Simulation: Transactions of The Society for Modeling and Simulation International and Simulation Modelling Practice and Theory (formerly Simulation Practice and Theory). The study covered the first 6 years of this century (2000)(2001)(2002)(2003)(2004)(2005) and included 576 papers. The key observation of this work was the relative lack of 'real world' involvement in simulation modelling research and an even greater lack of evidence of 'real world' benefit, arguably very alarming outcomes for an applied field. To further investigate this observation two additional surveys were carried out, one to study if real world papers appeared in the more widely known OR/MS literature (837 papers in 12 journals) and one to study if such papers appeared in Manufacturing and Logistics, an application area closely associated with simulation modelling (1077 papers in 10 journals). The results of these surveys confirmed our observations. We ask if this is the natural evolution of a field that has existed for half a century or an indication of a worrying problem? This paper reports on our findings and discusses whether or not simulation modelling research urgently needs to face a 'reality check.'

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Section: Studies Of Simulation Modelling Literaturementioning

confidence: 99%

Simulation modelling is 50! Do we need a reality check?

Taylor

Eldabi

Riley

et al. 2009

Journal of the Operational Research Society

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show abstract

“…Besides of the techniques and issues in this paper, there are numerous interesting topics in simulation output analysis such as initialization problems, comparison of systems, etc. For these topics you can refer to [4,6,7,9] and references therein.…”

Section: Discussionmentioning

confidence: 99%

Output Data Analysis of Simulation: A Review

Chang¹

2012

Journal of the Korea Society for Simulation

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Simulation is the imitation of the operation of a real-world process or system over time. It concerns the study of the operating characteristics of real systems. Typically, a simulation project consists of several steps such as data collection, coding, model verification, model validation, experimental design, output data analysis, and implementation. Among these steps of a simulation study this paper focus on statistical analysis methods of simulation output data. Specially, we explain how to develop confidence interval estimators for mean μ in terminating and non-terminating simulation cases. We, then, explore the estimation techniques for f(μ), where the function f(•) is a nonlinear that is continuously differentiable in a neighborhood of μ with f'(μ) ≠0.

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“…The pressure, temperature, volume and energy of the ensemble are the statistical averages over many time steps. The standard deviation in the mean was calculated from the autocovariance function [108]. To calculate the equilibrium volume with the NVT ensemble, calculations were run at multiple fixed volumes.…”

Section: Details Of Computational Methodsmentioning

confidence: 99%

Modeling Solute Thermokinetics in LiCI-KCI Molten Salt for Nuclear Waste Separation

Morgan¹,

Eapen²

2013

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A Comprehensive Review of Methods for Simulation Output Analysis

Cited by 18 publications

References 49 publications

Simulation modelling is 50! Do we need a reality check?

Simulation modelling is 50! Do we need a reality check?

Output Data Analysis of Simulation: A Review

Modeling Solute Thermokinetics in LiCI-KCI Molten Salt for Nuclear Waste Separation

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