In this paper we investigate the use of the average unit run length (AURL) as an important measure of the effectiveness of various quality control charting schemes. In particular we focus on its appropriateness for normally distributed processes that tend to produce units (or measurements) at slow rates. In our investigations with the standard Shewhart X and R charts, as well as the CUSUM chart, AURL shows that a sample size of n=1 can yield the fastest means of detecting shifts.

Nonparametric global optimization methods have been developed that determine the location of their next guess based on the rank-transformed objective function evaluations rather than the actual function values themselves. Another commonly-used transformation in nonparametric statistics is the normal score transformation. This paper applies the normal score transformation to the multi-univariate method of global optimization. The benefits of the new method are shown by its performance on a standard set of global optimization test problems. The normal score transformation yields a method that gives equivalent searches for any monotonic transformation of the objective function.