Latin supercube sampling for very high-dimensional simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
A generalized discrepancy and quadrature error bound
Mathematics of Computation
Proceedings of the 30th conference on Winter simulation
Introduction to Linear Optimization
Introduction to Linear Optimization
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Mathematics of Computation
Linear Programming and Network Flows
Linear Programming and Network Flows
Very large fractional factorial and central composite designs
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Introduction to Operations Research and Revised CD-ROM 8
Introduction to Operations Research and Revised CD-ROM 8
Design and Analysis of Experiments
Design and Analysis of Experiments
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Work smarter, not harder: a tutorial on designing and conducting simulation experiments
Proceedings of the Winter Simulation Conference
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We present a new method for constructing nearly orthogonal Latin hypercubes that greatly expands their availability to experimenters. Latin hypercube designs have proven useful for exploring complex, high-dimensional computational models, but can be plagued with unacceptable correlations among input variables. To improve upon their effectiveness, many researchers have developed algorithms that generate orthogonal and nearly orthogonal Latin hypercubes. Unfortunately, these methodologies can have strict limitations on the feasible number of experimental runs and variables. To overcome these restrictions, we develop a mixed integer programming algorithm that generates Latin hypercubes with little or no correlation among their columns for most any determinate run-variable combination—including fully saturated designs. Moreover, many designs can be constructed for a specified number of runs and factors—thereby providing experimenters with a choice of several designs. In addition, our algorithm can be used to quickly adapt to changing experimental conditions by augmenting existing designs by adding new variables or generating new designs to accommodate a change in runs.