Searching for important factors: sequential bifurcation under uncertainty
Proceedings of the 29th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Comparison with a standard via fully sequential procedures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Military applications of agent-based simulations
WSC '04 Proceedings of the 36th conference on Winter simulation
A two-phase screening procedure for simulation experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
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
Better than a petaflop: the power of efficient experimental design
Proceedings of the 40th Conference on Winter Simulation
Efficient experimental design tools for exploring large simulation models
Computational & Mathematical Organization Theory
Better than a petaflop: the power of efficient experimental design
Winter Simulation Conference
Work smarter, not harder: a tutorial on designing and conducting simulation experiments
Proceedings of the Winter Simulation Conference
Better than a petaflop: the power of efficient experimental design
Proceedings of the Winter Simulation Conference
Combining strong and screening designs for large-scale simulation optimization
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
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Analysts examining complex simulation models often conduct screening experiments to identify important factors. The controlled sequential bifurcation screening procedures CSB and CSB-X use a sequence of tests to classify factors as important or unimportant, while controlling Type I error and power. These procedures require analysts to identify the directions of the effects prior to experimentation, which can be problematic. We propose hybrid two-phase approaches, FFCSB and FFCSBX, as alternatives. Phase 1 uses an efficient fractional factorial to estimate factor effect directions; phase 2 uses CSB or CSB-X. Empirical investigations show these outperform CSB(X) in efficiency and effectiveness for many situations of practical interest.