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Some theoretical and practical aspects of common random numbers (CRN) for variance reduction in simulation analysis are considered. A simple proof of the optimality of CRN is presented and the efficiency of this technique for variance reduction is discussed. Applications of CRN to production...
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This article studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination...
Persistent link: https://www.econbiz.de/10004973517
The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most...
Persistent link: https://www.econbiz.de/10011097692
This tutorial gives a survey of strategic issues in the statistical design and analysis of experiments with deterministic and random simulation models. These issues concern validation, what-if analysis, optimization, and so on. The analysis uses regression models and least-squares algorithms....
Persistent link: https://www.econbiz.de/10011050197
To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or multivariate Kriging (Gaussian process) metamodels. In multivariate Kriging we face a major problem: the covariance matrix of all responses should remain positive-definite; we...
Persistent link: https://www.econbiz.de/10011052688
This article presents a novel heuristic for constrained optimization of computationally expensive random simulation models. One output is selected as objective to be minimized, while other outputs must satisfy given threshold values. Moreover, the simulation inputs must be integer and satisfy...
Persistent link: https://www.econbiz.de/10008483389
Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by...
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