Compromise designs under baseline parameterization

•The paper studies a class of compromise designs from adding runs to one-factor-at-a-time designs.•A theoretical result is established for the case of adding one run.•A complete search algorithm is developed to find optimal designs for adding two or more runs.

We consider estimation of main effects using two-level fractional factorial designs under the baseline parameterization. Previous work in the area indicates that orthogonal arrays are more efficient than one-factor-at-a-time designs whereas the latter are better than the former in terms of minimizing the bias due to non-negligible interactions. Using efficiency criteria, this paper examines a class of compromise designs obtained by adding runs to one-factor-at-a-time designs. A theoretical result is established for the case of adding one run. For adding two or more runs, we develop a complete search algorithm to find optimal compromise designs.