Taguchi and Robust Optimization

This report is intended to facilitate dialogue between engineers and optimizers about the eeciency of Taguchi methods for robust design, especially in the context of design by computer simulation. Three approaches to robust design are described: 1. Robust optimization, i.e. specifying an objective function f and then minimizing a smoothed (robust) version of f by the methods of numerical optimization. 2. Taguchi's method of specifying the objective function as a certain signal-to-noise ratio, to be optimized by designing, performing and analyzing a single massive experiment. 3. Specifying an expected loss function f and then minimizing a cheap-to-compute surrogate objective function ^ f , to be obtained by designing and performing a single massive experiment. Some relations between these approaches are noted and it is emphasized that only the rst approach is capable of iteratively progressing toward a solution.