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In this paper we develop strategies for integrating certain well-known variance reduction techniques to estimate a mean respoinse in a finite-horizon simulation experiment. Our building blocks are the techniques of conditional expectation, correlation induction, and control variates. Under some mild assumptions, we explain how each integrated strat,egy yields a smaller response variance than its constituent variance reduction techniques yield individually. We also provide asymptotic variance comparisons for integrated strategies involving the correlation-induction technique of Latin hypercube sampling. Our Monte Carlo results show that in the simulation of stochastic activity networks, large efficiency gains can be achieved by using these integrated variance reduction strategies.

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