Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Taguchi's parameter design: a panel discussion
Technometrics
Some Methods for Nonlinear Multi-objective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A comparison of complete global optimization solvers
Mathematical Programming: Series A and B
Kriging interpolation in simulation: a survey
WSC '04 Proceedings of the 36th conference on Winter simulation
Optimization of large simulations using statistical software
Computational Statistics & Data Analysis
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Classical and imprecise probability methods for sensitivity analysis in engineering: A case study
International Journal of Approximate Reasoning
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Technical Note---A Risk-Averse Newsvendor Model Under the CVaR Criterion
Operations Research
Constructing Risk Measures from Uncertainty Sets
Operations Research
Computers and Industrial Engineering
Robust Optimization for Unconstrained Simulation-Based Problems
Operations Research
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Stochastic Kriging for Simulation Metamodeling
Operations Research
Metamodel variability analysis combining bootstrapping and validation techniques
Proceedings of the Winter Simulation Conference
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Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a “robust” methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging.