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Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment. Journal of Electrical Technology UMY, 2017, 1(1):1-9. Distribution network optimization based on genetic algorithm. An immune genetic algorithm for inter-cell layout problem in cellular manufacturing system. Solving vehicle routing problem in the logistics distribution based on immune genetic algorithm//WHICEB, 2016:23. Second-order nearly orthogonal Latin hypercubes for exploring stochastic simulations. Quantile estimation with Latin hypercube sampling. Progressive Latin hypercube sampling:An efficient approach for robust sampling-based analysis of environmental models. A spatial conditioned Latin hypercube sampling method for mapping using ancillary data. Fat Latin hypercube sampling and efficient sparse polynomial chaos expansion for uncertainty propagation on finite precision models:Application to 2D deep drawing process//Computational Methods for Solids and Fluids, Springer International Publishing, 2016:185-213. Lebon J, Le Quilliec G, Breitkopf P, et al. American Society for Quality Control and American Statistical Association, 2000. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Hierarchical collaborative location and allocatio of emergency equipment based on scenario analysis. Transportation Research Part E:Logistics and Transportation Review, 2014, 69:160-179.
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Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Hierarchical facility location problem:Models, classifications, techniques, and applications. Farahani R Z, Hekmatfar M, Fahimnia B, et al. Logistics distribution center allocation based on ant colony optimization. Incorporating location, routing and inventorydecisions in supply chain network design. International Journal of Physical Distribution & Logistics Management, 2016, 46(6/7):562-583. Retail logistics in the transition from multi-channel to omni-channel. The design of reverse distribution networks:Models and solution procedures. For this reason, we employ this method to sample in the domain of the program input in this study, and denote the set of obtained samples as I. Transportation Science, 2007, 41(4):484-499. Latin hypercube sampling is an important approach of sampling a domain, with its advantage of reducing the number of samples and evenly sampling in the domain. A facility location model for bidirectional flows. We present numerical results comparing the various CIs.Rigby D. We establish the asymptotic validity of the CIs by first proving that the quantile estimators satisfy Bahadur representations, which show that the quantile estimators can be approximated by linear transformations of estimators of the cumulative distribution function. On average, our rLHS CIs are shorter than previous rLHS CIs and only slightly wider than the ssLHS CI.
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For rLHS, we construct CIs using batching and sectioning. We develop a consistent estimator of the asymptotic variance of the ssLHS quantile estimator’s central limit theorem, enabling us to provide the first confidence interval (CI) for a quantile when applying ssLHS. We consider single-sample LHS (ssLHS), which minimizes the variance that can be obtained from LHS, and also replicated LHS (rLHS). We examine quantile estimators obtained using simulation with Latin hypercube sampling (LHS), a variance-reduction technique that efficiently extends stratified sampling to higher dimensions and produces negatively correlated outputs. Quantiles are often used to measure risk of stochastic systems.