This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming presents a comparative computational study. It shows conditions under which with AV LHS satisfy finite stopping guarantees are asymptotically valid, discussing detail. computationally compares their use both non-sequent...

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regressio...

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models. This review surveys ‘classic’ and ‘modern’ designs for experiments with simulation models. Classic designs were developed for real, nonsimulated systems in agriculture, engineering, etc. These designs assume ‘a few’ factors (no more than 10 factors) with only ‘a few’ values per factor (no more than ...

Memetic algorithms, one type of algorithms inspired by nature, have been successfully applied to solve numerous optimization problems in diverse fields. In this paper, we propose a new memetic computing model, using a hierarchical particle swarm optimizer (HPSO) and latin hypercube sampling (LHS) method. In the bottom layer of hierarchical PSO, several swarms evolve in parallel to avoid being t...

Model Predictive Control (MPC) is a computationally demanding control technique that allows dealing with multiple-input and multiple-output systems, while handling constraints in a systematic way. The necessity of solving an optimization problem at every sampling instant often (i) limits the application scope to slow dynamical systems and/or (ii) results in expensive computational hardware impl...

The following techniques for uncertainty and sensitivity analysis are briefly summarized: Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol’ variance decomposition, and fast probability integration. Desirable features of Monte Carlo analysis in conjunction with Latin hypercube sampling are described in discussions of the followi...

We propose a scheme for producing Latin hypercube samples that can enhance any of the existing sampling algorithms in Bayesian networks. We test this scheme in combination with the likelihood weighting algorithm and show that it can lead to a significant improvement in the convergence rate. While performance of sampiing algorithms in general depends on the numerical properties of a network, in ...

Interest in research analyzing and predicting energy loads consumption the early stages of building design using meta-models has constantly increased recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this study, Latin Hypercube Sampling (LHS) is proposed as an alternative Fractional Factor Design (FFD), s...

In this study, a wavy microchannel heat sink with grooves using water as the working fluid is proposed for application to cooling microprocessors. The geometry of was optimized improve transfer and pressure loss simultaneously. To achieve optimization goals, average friction factor thermal resistance were used objective functions. Three dimensionless parameters selected design variables: distan...