Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Bootstrap methods in computer simulation experiments
WSC '95 Proceedings of the 27th conference on Winter simulation
Uniform and bootstrap resampling of empirical distributions
WSC '93 Proceedings of the 25th conference on Winter simulation
Control of initialization bias in multivariate simulation response
Communications of the ACM - Special issue on simulation modeling and statistical computing
Time-Varying Data Visualization Using Information Flocking Boids
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Interconnected DES models of emergency, outpatient, and inpatient departments of a hospital
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Simulating multivariate time series using flocking
Proceedings of the Winter Simulation Conference
Data-driven simulation of complex multidimensional time series
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
| Hi-index | 0.00 |
In most realistic simulations there are multiple outputs of interest and the overall performance of the system can only be estimated in terms of these multiple outputs. We propose a method that uses agent-based modeling to determine a truncation point to remove significant initialization bias. Mapping the output of multiple replications into agent paths that traverse the sample space helps determine when a near steady state has been reached. By viewing these paths in reversed time, qualitative and quantitative methods can be used to determine when the multivariate output is leaving its near-steady state regime as the paths coalesce back towards their common initialization state. The methodology is more efficient and general than typical approaches for finding a truncation point for scalar outputs of individual replicates. Artificial bootstrap-like re-sampling of simulation runs is proposed for expensive simulations to estimate system performance sensitivity.