An approach to ranking and selection for multiple performance measures

We develop a ranking and selection procedure for making multiple comparisons of systems that have multiple performance measures. The procedure combines multiple attribute utility theory with ranking and selection to select the best configuration from a set of K configurations using the indifference zone approach. We demonstrate our procedure on a simulation model of a large project that has six performance measures.