Design optimization using signal-to-noise ratio

This paper shows how design optimization can be achieved using signal-to-noise (S/N) ratios. A case of maintenance float policy is used to illustrate the application presented here. Basically, this involves the implementation of a robust design plan in simulation analysis. The design plan is based on the use of orthogonal arrays introduced by Taguchi. Through the application of Taguchi's S/N ratio, we demonstrate that the best design plan from an experimental design can be determined. This has several implications: (1) It reduces the experimentation time, (2) it can identify a fractional design that contains the best design plan and that design plan could be studied for full experimentation, (3) within a subset of a fractional design plan, the best design point can be found, and (4) the cost of experimentation is significantly reduced since minimal number of runs is required to identify the best design point. Finally, this important result helps experimenters to select a fractional design plan that contains the “best design point”.