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    Response surface designs are a core component of the response surface methodology, which is widely used in the context of product and process optimization. In this contribution, we present a new class of 3-level response surface designs, which can be viewed as matrices with entries equal to −1, 0 and +1. Because the new designs are orthogonal for the main effects and exhibit no aliasing between the main effects and the second-order effects (two-factor interactions and quadratic effects), we call them orthogonal minimally aliased response surface designs or OMARS designs. We constructed a catalog of 55,531 OMARS design for 3 to 7 factors using integer programming techniques. Also, we characterized each design in the catalog extensively in terms of estimation and prediction efficiency, power, fourth-order correlations, and projection capabilities, and we identified interesting designs and investigated trade-offs between the different design evaluation criteria. Finally, we developed a multi-attribute decision algorithm to select designs from the catalog. Important results of our study are that we discovered some novel designs that challenge standard response surface designs and that our catalog offers much more flexibility than the standard designs currently used.

  • : Peter Goos and José Núñez Ares
  • : KU Leuven
  • : Peter Goos
  • : experimental_design
  • : intermediate
  • : peter.goos@kuleuven.be
  • : +32484107894
OMARS Designs: Bridging the Gap between Definitive Screening Designs and Standard Response Surface Designs