WSC '94 Proceedings of the 26th conference on Winter simulation
Bayesian analysis for simulation input and output
Proceedings of the 29th conference on Winter simulation
Problems in Bayesian analysis of stochastic simulation
WSC '86 Proceedings of the 18th conference on Winter simulation
Steps to implement Bayesian input distribution selection
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
Accounting for input model and parameter uncertainty in simulation
Proceedings of the 33nd conference on Winter simulation
Input uncertainty: accounting for parameter uncertainty in simulation input modeling
Proceedings of the 33nd conference on Winter simulation
Resampling methods for input modeling
Proceedings of the 33nd conference on Winter simulation
Calculation of confidence intervals for simulation output
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Input modeling: input model uncertainty: why do we care and what should we do about it?
Proceedings of the 35th conference on Winter simulation: driving innovation
Reducing parameter uncertainty for stochastic systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
On the Accuracy of Ad Hoc Distributed Simulations for Open Queueing Network
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
Multiple input and multiple output simulation metamodeling using Bayesian networks
Proceedings of the Winter Simulation Conference
| Hi-index | 0.01 |
Uncertainty associated with input parameters and models in simulation has gained attentions in recent years. The sources of uncertainties include lack of data and lack of knowledge about physical systems. In this paper, we present a new reliable simulation mechanism to help improve simulation robustness when significant uncertainties exist. The new mechanism incorporates variabilities and uncertainties based on imprecise probabilities, where the statistical distribution parameters in the simulation are intervals instead of precise real numbers. The mechanism generates random interval variates to model the inputs. Interval arithmetic is applied to simulate a set of scenarios simultaneously in each simulation run. To ensure that the interval results bound those from the traditional real-valued simulation, a generic approach is also proposed to specify the number of replications in order to achieve the desired robustness. This new reliable simulation mechanism can be applied to address input uncertainties to support robust decision making.