A review of advanced methods for simulation output analysis

Alexopoulos, C.

doi:10.1109/wsc.1994.717092

Cited by 5 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Much research has focused on the statistical analysis of simulation output, especially for the identification of appropriate distributions to describe the output data (Alexopoulos 1994, Charnes et al 1994. The output statistics of a simulation system are relatively standard between the available simulation language systems.…”

Section: State Of the Art Reviewmentioning

confidence: 99%

Performance measurement indices for simulated construction operations

McCabe¹,

AbouRizk²

2001

Can. J. Civ. Eng.

View full text Add to dashboard Cite

This paper describes the development of indices useful in automating the experimentation process of a computer simulation. Simulation methodologies have been developed to model construction systems, but most of these systems require the experimentation process to be carried out manually. In achieving optimum performance, one has to repeat an exhaustive number of experiments. The indices can be used to automate this process, as they are indicators of bottlenecks in the system that can be tracked through the simulation output. They are based upon user-defined performance guidelines for the resources. Where a performance index falls outside the acceptable range, remedial action may be taken. Belief networks, a probabilistic form of artificial intelligence, were used to automate the analysis of the indices to determine the most likely causal factor of poor performance.Key words: construction engineering, computer simulation, performance indices, belief networks, performance improvement.

show abstract

Section: State Of the Art Reviewmentioning

confidence: 99%

Performance measurement indices for simulated construction operations

McCabe¹,

AbouRizk²

2001

Can. J. Civ. Eng.

View full text Add to dashboard Cite

show abstract

“…But they're all explained in detail elsewhere; see Alexopoulos (1993Alexopoulos ( , 1994, chapter 11 of Banks and Carson (1984), chapter 3 of Bratley, Fox, and Schrage (1987), Charnes (1993), chapters 2, 3, and 5 of Fishman (1978), Goldsman (1992), chapter 7 of Khoshnevis (1994)) Kleijnen (1987)) chapter 9 of Law and Kelton (1991), Lewis and Orav (1989), chapter 6 of Ripley (1987), Seila (1991Seila ( , 1992, and chapter 6 of Thesen and Travis (1992). A~ak.,3n,wac n, l ha h\no:e "1 "J '"~'La.…”

Section: Regenerationmentioning

confidence: 99%

Statistical analysis of simulation output

Kelton

1997

Proceedings of the 29th Conference on Winter Simulation - WSC '97

View full text Add to dashboard Cite

This paper describes, in general terms, methods to help design the runs for simulation models and interpreting their output. Statistical methods are described for several different purposes, and related probiemS iike COmparisOn, ..~ -1_ --_ __ ~..^L: ^_ _^__ vdridnce ~~UUCLIWII, sen sitivity estimation, metamodeling, and optimization are mentioned. The main point is to call attention to the challenges and opportunities in using simulation models carefully and effectively.

show abstract

“…But they're all explained in detail elsewhere; see Alexopoulos (1993Alexopoulos ( , 1994, chapter 11 of Banks and Carson (1984), chapter 3 of Bratley, Fox, and Schrage (1987), Charnes (1993), chapters 2,3, and 5 of Fishman (1978), Goldsman (1992), chapter 7 of Khoshnevis ( 1994), Kleijnen (1987), chapter 9 of Law and Kelton (1991), Lewis and Orav ( 1989), chapter 6 of Ripley (1987), Seila (1991Seila ( , 1992, and chapter 6 of Thesen and Travis (1992).…”

Section: Keltonmentioning

confidence: 99%

A tutorial on design and analysis of simulation experiments

Kelton

1995

Proceedings of the 27th Conference on Winter Simulation - WSC '95

View full text Add to dashboard Cite

This paper describes, in general terms, methods to help design the runs for simulation modeIs and interpreting their output. Statistical methods are described for several different purposes, and related problems like comparison, variance reduction, sensitivity estimation, metamodeling, and optimization are mentioned. The main point is to call attention to the challenges and opportunities in using simulation models carefully and effectively. This paper is an update of Kelton (1994).

show abstract

A review of advanced methods for simulation output analysis

Cited by 5 publications

References 25 publications

Performance measurement indices for simulated construction operations

Performance measurement indices for simulated construction operations

Statistical analysis of simulation output

A tutorial on design and analysis of simulation experiments

Contact Info

Product

Resources

About