Using integer programming techniques for the solution of an experimental design problem

Latin hypercube sampling is often used to estimate the distribution function of a complicated function of many random variables. In so doing, it is typically necessary to choose a permutation matrix which minimizes the correlation among the cells in the hypercube layout. This problem can be formulated as a generalized, multi-dimensional assignment problem. For the two-dimensional case, the authors provide a polynomial algorithm. For higher dimensions, they offer effective heuristic and bounding procedures.