| Summary: | It is generally known that blocking can reduce unexplained variation, and in response surface designs block sizes can
be pre-specified. This paper proposes a novel way of weighting D-optimality criteria obtained from all possible models to
construct robust designs with blocking factors and addresses the challenge of uncertainty as to whether a first-order model, an
interaction model, or a second-order model is the most appropriate choice. Weighted D-optimal designs having 2 and 3 variables
with 2, 3, and 4 blocks are compared with corresponding standard D-optimal designs in terms of the
DN
-efficiencies. Effects of
blocking schemes are also investigated. Both an exchange algorithm (EA) and a genetic algorithm (GA) are employed to generate
the model-robust designs. The results show that the proposed
Dw
-optimality criterion can be a good alternative for researchers as
it can create robust designs across the set of potential models.
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