We propose "low cost response surface methods" (LCRSM) that typically require half the experimental runs of standard response surface methods based on central composite and Box Behnken designs (G. Box and D.W. Behnken, 1960) but yield comparable or lower modeling errors under realistic assumptions. In addition, the LCRSM methods have substantially lower modeling errors and greater expected savings compared with alternatives with comparable numbers of runs, including small composite designs and computer-generated designs based on popular criteria such as D-optimality. Therefore, when simulation runs are expensive, low cost response surface methods can be used to create regression meta-models for queuing or other system optimization. The LCRSM procedures are also apparently the first experimental design methods derived as the solution to a simulation optimization problem. For these reasons, we say that LCRSM are "for and from" simulation optimization. We compare the proposed LCRSM methods with a large number of alternatives based on six criteria. We conclude that the proposed methods offer an attractive alternative to current methods in many relevant situations.
['Theodore T. Allen', 'Liyang Yu']
Low cost response surface methods for and from simulation optimization