Algorithms for finding generalized minimum aberration designs

TL;DR

This paper develops two algorithms to efficiently find generalized minimum aberration orthogonal arrays, discovering new arrays and validating heuristics for approximating optimal designs in experimental setups.

Contribution

It introduces two directed enumeration algorithms utilizing integer programming with isomorphism pruning to find GMA arrays, including 16 previously unknown arrays.

Findings

Discovered 16 new GMA arrays not in literature.

Validated heuristics for quick near-GMA array generation.

Demonstrated efficiency of algorithms within reasonable computational resources.

Abstract

Statistical design of experiments is widely used in scientific and industrial investigations. A generalized minimum aberration (GMA) orthogonal array is optimum under the well-established, so-called GMA criterion, and such an array can extract as much information as possible at a fixed cost. Finding GMA arrays is an open (yet fundamental) problem in design of experiments because constructing such arrays becomes intractable as the number of runs and factors increase. We develop two directed enumeration algorithms that call the integer programming with isomorphism pruning algorithm of Margot (2007) for the purpose of finding GMA arrays. Our results include 16 GMA arrays that were not previously in the literature, along with documentation of the efficiencies that made the required calculations possible within a reasonable budget of computer time. We also validate heuristic algorithms against a GMA array catalog, by showing that they quickly output near GMA arrays, and then use the heuristics to find near GMA arrays when enumeration is computationally burdensome.