Production leveling (heijunka) is one of the lean tool that is used to stabilize a manufacturing system (reduce the bullwhip effect). Production leveling is a kind of cyclic scheduling that creates production regularity and coordination simplicity. This article describes simulation modeling and multivariate analysis of an assembly production line in a microelectronics factory. The assembly line...

Consider the problem of drawing random variates (X1, . . . , Xn) from a distribution where the marginal of each Xi is specified, as well as the correlation between every pair Xi and Xj . For given marginals, the Fréchet-Hoeffding bounds put a lower and upper bound on the correlation between Xi and Xj . Hence any achievable correlation can be uniquely represented by a convexity parameter λij ∈ [...

Randomisation schemes are rules that assign patients to treatments in a clinical trial. Many of these schemes have the common aim of maintaining balance in the numbers of patients across treatment groups. The properties of imbalance that have been investigated in the literature are based on two treatment groups. In this paper, their properties for K > 2 treatments are studied for two randomisat...

In fMRI data analysis, univariate techniques have been used to detect activation regions. In this study, we propose and compare an alternative approach, multivariate techniques, to extract meaningful activation patterns from fMRI data. When multivariate techniques such as PCA, rPCA, sICA, tICA, and FA were applied to the simulated fMRI-like data, only rPCA and FA extracted meaningful patterns i...

Lattice rules are among the best methods to estimate integrals in a large numberof dimensions. They are part of the quasi-Monte Carlo set of tools. A new class of lattice rules,defined in a space of polynomials with coefficients in a finite field, is introduced in this paper, anda theoretical framework for these polynomial lattice rules is developed. A randomized version isstudi...

Several approaches to robust design have been proposed in the past. Only few acknowledged the paradigm shift from performance based design to design for cost. The incorporation of economics in the design process, however, makes a probabilistic approach to design necessary, due to the inherent ambiguity of assumptions and requirements as well as the operating environment of future aircraft. The ...

Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex netwo...

When we use simulations to estimate the performance of stochastic systems, the simulation is often driven by input models estimated from finite real-world data. A complete statistical characterization of system performance estimates requires quantifying both input model and simulation estimation errors. The components of input models in many complex systems could be dependent. In this paper, we...

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters si...

To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or multivariate Kriging (Gaussian process) metamodels. In multivariate Kriging we face a major problem: the covariance matrix of all responses should remain positive-definite; we therefore use the recently proposed “nonseparable dependence” model. To evaluate the performance of univ...