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Optimization of large-scale complex systems often involves high-fidelity computational simulation models that are very time-consuming. As a result, the number of objective function evaluations is often very limited and presents a major hurdle for optimization. Previous works on a new framework known as ordinal transformation (OT) provides a method that makes use of a low-fidelity approximate model...
In Peng et al. (2015b), we show that the probability of correct selection (PCS), a commonly used metric, is not necessarily monotonically increasing with respect to the number of simulation replications. A simple counterexample where the PCS may decrease with additional sampling is provided to motivate the problem. The reference identifies the induced correlations as the source of the non-monotonicity,...
For the statistical selection problem we formulate a general framework comprising both sequential sampling allocation and optimal design selection, for which the traditional probability of correct selection measure is inadequate. Therefore, we introduce the integrated probability of correct selection to better characterize the objective. In this framework, the usual selection policy of choosing the...
Common random numbers and the standard clock method are examples of effective variance reduction techniques that also share information and simulation resources when generating realizations of different simulated systems whose performances are being compared. This sharing of computing resources and the potentially widely different computational requirements for different simulation models are important...
Consider the context of selecting an optimal system from amongst a finite set of competing systems, based on a “stochastic” objective function and subject to a single “stochastic” constraint. In this setting, and assuming the objective and constraint performance measures have a bivariate normal distribution, we present a characterization of the optimal sampling allocation across systems. Unlike previous...
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