Design of experiments: robust design: seeking the best of all possible worlds
Proceedings of the 32nd conference on Winter simulation
Simulation with Arena
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Very large fractional factorial and central composite designs
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Data farming: discovering surprise
WSC '04 Proceedings of the 36th conference on Winter simulation
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
Defense and homeland security applications of multi-agent simulations
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Two-phase screening procedure for simulation experiments
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
INFORMS Journal on Computing
Better than a petaflop: the power of efficient experimental design
Winter Simulation Conference
Knowledge-Based Simulation Experiment Data Integrative Analysis Technology
PADS '12 Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation
Better than a petaflop: the power of efficient experimental design
Proceedings of the Winter Simulation Conference
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Recent advances in high-performance computing have pushed computational capabilities to a petaflop (a thousand trillion operations per second) in a single computing cluster. This breakthrough has been hailed as a way to fundamentally change science and engineering by letting people perform experiments that were previously beyond reach. But for those interested in exploring the I/O behavior of their simulation model, efficient experimental design has a much higher payoff at a much lower cost. A well-designed experiment allows the analyst to examine many more factors than would otherwise be possible, while providing insights that cannot be gleaned from trial-and-error approaches or by sampling factors one at a time. We present the basic concepts of experimental design, the types of goals it can address, and why it is such an important and useful tool for simulation. Ideally, this tutorial will entice you to use experimental designs in your upcoming simulation studies.