TY  - JOUR
IS  - 3
JF  - Informs Journal on Computing
PB  - Informs
ID  - eprints1436
EP  - 484
N2  - 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. 
SN  - 1091-9856
KW  - statistics; design of experiments; inventory production; simulation; decision analysis; risk
UR  - http://joc.journal.informs.org/cgi/content/abstract/ijoc.1110.0465v1
A1  - Dellino, Gabriella
A1  - Kleijnen, Jack P.C.
A1  - Meloni, Carlo
Y1  - 2012/08//
VL  - 24
TI  - Robust optimization in simulation: Taguchi and Krige combined
SP  - 471
AV  - public
ER  -