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.
|Title of host publication||Uncertainty Management in Simulation-Optimization of Complex Systems|
|Subtitle of host publication||Algorithms and Applications|
|Editors||Gabriella Dellino, Carlo Meloni|
|Publication status||Published - 2015|
|Name||Operations Research/Computer Science Interfaces Series|
Dellino, G., Meloni, C., & Kleijnen, J. P. C. (2015). Metamodel-based robust simulation-optimization: An overview. In G. Dellino, & C. Meloni (Eds.), Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications (Vol. 59, pp. 27-54). (Operations Research/Computer Science Interfaces Series; Vol. 59). Springer VS. https://doi.org/10.1007/978-1-4899-7547-8_2