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In this article, we develop a budget allocation approach that can efficiently employ the potentially tight simulation resource to construct a percentile confidence interval quantifying the impact of the input uncertainty on the system performance estimates, while controlling the simulation estimation error. Specifically, nonparametric bootstrap is used to generate samples of input models quantifying both the input distribution family and parameter value uncertainty. Then, the direct simulation is used to propagate the input uncertainty to the output response. Since each simulation run could be computationally expensive, given a tight simulation budget, we propose an efficient budget allocation approach that can balance the finite sampling error introduced by using finite bootstrapped samples to quantify the input uncertainty and the system response estimation error introduced by using finite replications to estimate the system response at each bootstrapped sample. Our approach is theoretically supported, and empirical studies also demonstrate that it has better and more robust performance than direct bootstrapping.<\/jats:p>","DOI":"10.1145\/3129148","type":"journal-article","created":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T12:48:13Z","timestamp":1509108493000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["An Efficient Budget Allocation Approach for Quantifying the Impact of Input Uncertainty in Stochastic Simulation"],"prefix":"10.1145","volume":"27","author":[{"given":"Yuan","family":"Yi","sequence":"first","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, NY"}]},{"given":"Wei","family":"Xie","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, NY"}]}],"member":"320","published-online":{"date-parts":[[2017,10,27]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proceedings of the 2007 INFORMS Simulation Society Research Workshop. 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