We consider the problem of finding a set of feasible or near-feasible systems among a finite number of simulated systems in the presence of constraints on secondary performance measures. We first present a generic procedure that detects the feasibility of one system in the presence of one constraint and extend it to the case of two or more systems and constraints. To accelerate the elimination of infeasible systems, a method that reuses collected observations and its varianceupdating version are discussed. Experimental results are presented to compare the performance of the proposed procedures.
We discuss an improved jackknifed Durbin-Watson estimator for the variance parameter from a steady-state simulation. The estimator is based on a combination of standardized time series area and Cramér-von Mises estimators. Various examples demonstrate its efficiency in terms of bias and variance compared to other estimators.
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