Design and Analysis of Experiments
Design and Analysis of Experiments
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Modern Regression Methods
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Better than a petaflop: the power of efficient experimental design
Winter Simulation Conference
The exponential expansion of simulation in research
Proceedings of the Winter Simulation Conference
Work smarter, not harder: a tutorial on designing and conducting simulation experiments
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
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Designed experiments are powerful ways to gain insights into the behavior of complex simulation models. In recent years, many new designs have been created to address the large number of factors and complex response surfaces that often arise in simulation studies, but handling discrete-valued or qualitative factors remains problematic. We proposed a framework for generating, with a (given) limited number of design points n, a design which is nearly orthogonal and also nearly balanced for any mix of factor types (categorical, numerical discrete, and numerical continuous) and/or mix of factor levels. Our approach can be used to create designs with low maximum absolute pairwise correlation, low imbalance level, and high D-optimality for simulation problems with mixed factor types. Our mixed designs are much more efficient than existing alternatives.