Abstract
A standard problem in the analysis of outputs from terminating simulations is the need to determine the number of replications needed to construct confidence intervals for performance indicators from the simulation (Law and Kelton, 2000). In traditional industrial applications of simulation such as manufacturing and queuing simulations a single mean for each performance indicator is all that is needed. In spatial simulations however, the problem is more complex as performance indicators can vary spatially as in the case of travel simulations where performance indicators for each destination must be analysed. This paper presents three alternative methods recommended in the simulation literature for determining the number of replications required to obtain confidence intervals based for a given alpha level and user defined confidence interval half width or relative preceision. The problem of measuring multiple performance indicators is addressed with a short discussion of the Bonferroni Correction. These methods are then adapted to spatial simulations using a travel simulation for Banff, Yoho, Kootenay and Jasper National Parks as an example. Outputs for daily link Use and daily link encounters are examined applying different values for absolute accuracy and relative precision. Conclusions are then drawn on the relationship between the sensitivity of performance indicators to random variables in the simulation model and the specification of absolute accuracy and relative precision for spatial dynamic simulation models.
| Original language | English (US) |
|---|---|
| Title of host publication | MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings |
| Pages | 141-148 |
| Number of pages | 8 |
| State | Published - 2005 |
| Event | International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, MODSIM05 - Melbourne, VIC, Australia Duration: Dec 12 2005 → Dec 15 2005 |
Other
| Other | International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, MODSIM05 |
|---|---|
| Country | Australia |
| City | Melbourne, VIC |
| Period | 12/12/05 → 12/15/05 |
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Keywords
- Simulation output analysis
- Simulation statistics
- Spatial simulation
ASJC Scopus subject areas
- Information Systems and Management
- Modeling and Simulation
Cite this
Generating confidence intervals for spatial simulations - Determining the number of replications for spatial terminating simulations. / Itami, Robert M.; Zell, Darrel; Grigel, Frank; Gimblett, Randy.
MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings. 2005. p. 141-148.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Generating confidence intervals for spatial simulations - Determining the number of replications for spatial terminating simulations
AU - Itami, Robert M.
AU - Zell, Darrel
AU - Grigel, Frank
AU - Gimblett, Randy
PY - 2005
Y1 - 2005
N2 - A standard problem in the analysis of outputs from terminating simulations is the need to determine the number of replications needed to construct confidence intervals for performance indicators from the simulation (Law and Kelton, 2000). In traditional industrial applications of simulation such as manufacturing and queuing simulations a single mean for each performance indicator is all that is needed. In spatial simulations however, the problem is more complex as performance indicators can vary spatially as in the case of travel simulations where performance indicators for each destination must be analysed. This paper presents three alternative methods recommended in the simulation literature for determining the number of replications required to obtain confidence intervals based for a given alpha level and user defined confidence interval half width or relative preceision. The problem of measuring multiple performance indicators is addressed with a short discussion of the Bonferroni Correction. These methods are then adapted to spatial simulations using a travel simulation for Banff, Yoho, Kootenay and Jasper National Parks as an example. Outputs for daily link Use and daily link encounters are examined applying different values for absolute accuracy and relative precision. Conclusions are then drawn on the relationship between the sensitivity of performance indicators to random variables in the simulation model and the specification of absolute accuracy and relative precision for spatial dynamic simulation models.
AB - A standard problem in the analysis of outputs from terminating simulations is the need to determine the number of replications needed to construct confidence intervals for performance indicators from the simulation (Law and Kelton, 2000). In traditional industrial applications of simulation such as manufacturing and queuing simulations a single mean for each performance indicator is all that is needed. In spatial simulations however, the problem is more complex as performance indicators can vary spatially as in the case of travel simulations where performance indicators for each destination must be analysed. This paper presents three alternative methods recommended in the simulation literature for determining the number of replications required to obtain confidence intervals based for a given alpha level and user defined confidence interval half width or relative preceision. The problem of measuring multiple performance indicators is addressed with a short discussion of the Bonferroni Correction. These methods are then adapted to spatial simulations using a travel simulation for Banff, Yoho, Kootenay and Jasper National Parks as an example. Outputs for daily link Use and daily link encounters are examined applying different values for absolute accuracy and relative precision. Conclusions are then drawn on the relationship between the sensitivity of performance indicators to random variables in the simulation model and the specification of absolute accuracy and relative precision for spatial dynamic simulation models.
KW - Simulation output analysis
KW - Simulation statistics
KW - Spatial simulation
UR - http://www.scopus.com/inward/record.url?scp=80053095804&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053095804&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0975840002
SN - 9780975840009
SP - 141
EP - 148
BT - MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings
ER -