Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Importance sampling for stochastic simulations
Management Science
Simulation: a statistical perspective
Simulation: a statistical perspective
Simulation and optimization in production planning: a case study
Decision Support Systems
Response surface methodology and its application in simulation
WSC '93 Proceedings of the 25th conference on Winter simulation
Theory of Modelling and Simulation
Theory of Modelling and Simulation
Five-stage procedure for the evaluation of simulation models through statistical techniques
WSC '96 Proceedings of the 28th conference on Winter simulation
Design-time simulation of a large-scale, distributed object system
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on Web-based modeling and simulation
Proceedings of the 32nd conference on Winter simulation
A framework for configurable hierarchical simulation in a multiple-user decision support environment
WSC '05 Proceedings of the 37th conference on Winter simulation
Designing what-if analysis: towards a methodology
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
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This paper is an advanced tutorial on the use of statistical techniques in sensitivity analysis, including the application of these techniques to optimization and validation of simulation models. Sensitivity analysis is divided into two phases. The first phase is a pilot stage, which consists of screening or searching for the important factors; a simple technique is sequential bifurcation. In the second phase, regression analysis is used to approximate the input/output behavior of the simulation model. This regression analysis gives better results when the simulation experiment is well designed, using classical statistical designs such as fractional factorials. To optimize the simulated system, Response Surface Methodology (RSM) is applied; RSM combines regression analysis, design of experiments, and steepest ascent. To validate a simulation model that lacks input/output data, again regression analysis and design of experiments are applied. Several case studies are summarized; they illustrate how in practice statistical techniques can make simulation studies give more general results, in less time.