In industrial experimentation, time and material costs are often at a premium. In designing an experiment, one needs to balance the desire for sufficient experimental runs to provide adequate data analysis, with the need to conduct a cost-effective experiment. A common compromise is the use of fractional factorial (FF) designs, in which only a fraction of the total possible runs is utilized. After discussing the basic concepts of FF designs, we introduce the fractional factorial split-plot (FFSP) design. Such designs occur frequently, because certain factors are often harder to vary than others, thus imposing randomization restric- tions. This thesis examines two techniques aimed at reducing run size that have not been greatly explored in the FFSP setting — nonregular designs and semifoldover designs. We show that these designs offer competitive alternatives to the more standard regular and full foldover designs and we produce tables of optimal designs in both scenarios.

In industrial experimentation, time and material costs are often at a premium. In designing an experiment, one needs to balance the desire for sufficient experimental runs to provide adequate data analysis, with the need to conduct a cost-effective experiment. A common compromise is the use of fractional factorial (FF) designs, in which only a fraction of the total possible runs is utilized. After discussing the basic concepts of FF designs, we introduce the fractional factorial split-plot (FFSP) design. Such designs occur frequently, because certain factors are often harder to vary than others, thus imposing randomization restric- tions. This thesis examines two techniques aimed at reducing run size that have not been greatly explored in the FFSP setting — nonregular designs and semifoldover designs. We show that these designs offer competitive alternatives to the more standard regular and full foldover designs and we produce tables of optimal designs in both scenarios.