  The purpose of order-of-addition (OofA) experiments is to identify the best
order in a sequence of m components in a system or treatment. Such experiments
may be analysed by various regression models, the most popular ones being based
on pairwise ordering (PWO) factors or on component-position (CP) factors. This
paper reviews these models and extensions and proposes a new class of models
based on response surface (RS) regression using component position numbers as
predictor variables. Using two published examples, it is shown that RS models
can be quite competitive. In case of model uncertainty, we advocate the use of
model averaging for analysis. The averaging idea leads naturally to a design
approach based on a compound optimality criterion assigning weights to each
candidate model.
