Recent work by Johnson et al. (J. Statist. Plann. Inference 26 (1990) t31-148) establishes
equivalence of the maximin distance design criterion and an entropy criterion motivated by
function prediction in a Bayesian setting. The latter criterion has been used by Currin et al.
(J. Amer. Statist. Assoc. 86 (1991) 953-963) to design experiments for which the motivating
application is approximation of a complex deterministic computer model. Because computer
experiments often have a large number of controlled variables (inputs), maximin designs of
moderate size are often concentrated in the corners of the cuboidal design region, i.e. each input
is represented at only two levels. Here we will examine some maximin distance designs
constructed within the class of Latin hypercube arrangements. The goal of this is to find designs
which offer a compromise between the entropy/maximin criterion, and good projective properties
in each dimension (as guaranteed by Latin hypercubes). A simulated annealing search
algorithm is presented for constructing these designs, and patterns apparent in the optimal
designs are discussed.