Statistical analysis of simulation output

Nakayama,

doi:10.1109/wsc.2008.4736056

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…While a histogram gives an approximate idea of the overall distribution, the Q-Q plot is more adequate to see how well a theoretical distribution fits the data set. Concerning numerical methods, Skewness measures the degree of symmetry of a probability distribution about its mean, and is a commonly used metric in the analysis of simulation output data (Sargent, 1976;Nakayama, 2008;Law, 2015). If skewness is positive, the distribution is skewed to the right, and if negative, the distribution is skewed to the left.…”

Section: Descriptivementioning

confidence: 99%

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Towards a standard model for research in agent-based modeling and simulation

Fachada

Lopes

Martins

et al. 2015

PeerJ Computer Science

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Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decisionmaking agent. ABMs are very sensitive to implementation details. Thus, it is very easy to inadvertently introduce changes which modify model dynamics. Such problems usually arise due to the lack of transparency in model descriptions, which constrains how models are assessed, implemented and replicated. In this paper, we present PPHPC, a model which aims to serve as a standard in agent based modeling research, namely, but not limited to, conceptual model specification, statistical analysis of simulation output, model comparison and parallelization studies. This paper focuses on the first two aspects (conceptual model specification and statistical analysis of simulation output), also providing a canonical implementation of PPHPC. The paper serves as a complete reference to the presented model, and can be used as a tutorial for simulation practitioners who wish to improve the way they communicate their ABMs.

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Section: Descriptivementioning

confidence: 99%

“…In practice, however, the situation is very different. While many ABMs have been published and simulation output analysis is a widely discussed subject matter (Sargent, 1976;Kelton, 1997;Law, 2007;Nakayama, 2008;Law, 2015), comprehensive inquiries concerning the output of ABM simulations are hard to find in the scientific literature.…”

Section: Introductionmentioning

confidence: 99%

Towards a standard model for research in agent-based modeling and simulation

Fachada

Lopes

Martins

et al. 2015

PeerJ Computer Science

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show abstract

“…Simulations are run for 900 seconds of real time. Each data point represents an average of twenty runs using different seeds with the corresponding confidence interval of 95% [14,15]. The overall simulation parameters are summarized in Table 1.…”

Section: Simulation Environmentmentioning

confidence: 99%

An Effective Capacity Estimation Scheme in IEEE802.11-based Ad Hoc Networks

Zafar

Harle

Andonović

et al. 2012

JART

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Capacity estimation is a key component of any admission control scheme required to support quality of serviceprovision in mobile ad hoc networks. A range of schemes have been previously proposed to estimate residualcapacity that is derived from window-based measurements of channel estimation. In this paper a simple and improvedmechanism to estimate residual capacity in IEEE802.11-based ad hoc networks is presented. The scheme proposesthe use of a ‘forgiveness’ factor to weight these previous measurements and is shown through simulation-basedevaluation to provide accurate utilizations estimation and improved residual capacity based admission control.

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“…Therefore, the model will produce a distribution of outputs over many runs using the same input data. Paper (Nakayama 2008)…”

Section: Model Verification and Validationmentioning

confidence: 99%

6.5.1 Maintenance Performance Modeling to Sustain System Operational Requirements for Complex Military Platforms

Kerr¹,

Nesterov

2010

INCOSE International Symp

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Both military and commercial asset management practices are receiving renewed scrutiny to reduce the logistic cost of ownership. Asset managers are being asked to analyse and improve on maintenance practices. This paper describes the application of computer modelling techniques to evaluate the effectiveness of existing and alternate maintenance solutions. Modelling using computer simulations is widely applied to new system development and allows complex processes and structures to be analysed and quantitatively evaluated. However, less work has been undertaken in applying modelling techniques to analyse existing in-service maintenance systems performance against potential alternatives.Specific characteristics of a stochastic model for complex military platforms have been identified to assure that model implementation accurately represents suggested maintenance processes. Systems Engineering methodology is used to create and analyse targeted maintenance models in question: reactive and proactive variants. Existing maintenance process has been described and performance measurement criteria have been identified. In conclusion the models' effectiveness has been evaluated and complemented by its sensitivity analysis. The objective is to present the case study of maintenance modelling applied for the complex military platform.

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Statistical analysis of simulation output

Abstract: We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Carlo simulations. Terminating and steady-state simulations are considered.

Cited by 14 publications

References 27 publications

Towards a standard model for research in agent-based modeling and simulation

Towards a standard model for research in agent-based modeling and simulation

An Effective Capacity Estimation Scheme in IEEE802.11-based Ad Hoc Networks

6.5.1 Maintenance Performance Modeling to Sustain System Operational Requirements for Complex Military Platforms

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