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Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
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Designing a screening experiment with a reciprocal Weibull degradation rate
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Stochastic kriging for simulation metamodeling
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ACM Transactions on Modeling and Computer Simulation (TOMACS)
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SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
A novel Artificial Immune System for fault behavior detection
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Simulation optimization using metamodels
Winter Simulation Conference
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Multi-Agent-Based simulation for analysis of transport policy and infrastructure measures
PRIMA'11 Proceedings of the 14th international conference on Agent Based Simulation for a Sustainable Society and Multi-agent Smart Computing
Future Generation Computer Systems
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Design Of Experiments (DOE) is needed for experiments with real-life systems, and with either deterministic or random simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random simulation. DOE may have two goals: sensitivity analysis and optimization. This contribution starts with classic DOE including 2k--p and Central Composite Designs (CCDs). Next, it discusses factor screening through Sequential Bifurcation. Then it discusses Kriging including Latin Hypercube Sampling and sequential designs. It ends with optimization through Generalized Response Surface Methodology and Kriging combined with Mathematical Programming, including Taguchian robust optimization.