An improved averaged two-replication procedure with Latin hypercube sampling |
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Authors: | Güzin Bayraksan |
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Affiliation: | Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH 43210, United States |
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Abstract: | The averaged two-replication procedure assesses the quality of a candidate solution to a stochastic program by forming point and confidence interval estimators on its optimality gap. We present an improved averaged two-replication procedure that uses Latin hypercube sampling to form confidence intervals of optimality gap. This new procedure produces tighter and less variable interval widths by reducing the sampling error by . Despite having tighter intervals, it improves an earlier procedure’s asymptotic coverage probability bound from to . |
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Keywords: | Stochastic optimization Solution validation Variance reduction Latin hypercube sampling Monte Carlo simulation |
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