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Design and analysis of industrial strip-plot experiments

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  • Arnouts H.
  • GOOS, Peter

Abstract

The cost of experimentation can often be reduced by forgoing complete randomization. A well-known design with restricted randomization is a split-plot design, which is commonly used in industry when some experimental factors are harder to change than others or when a two-stage production process is studied. Split-plot designs are also often used in robust product design to develop products that are insensitive to environmental or noise factors. Another, lesser known, type of experimental design plan that can be used in such situations is the strip-plot experimental design. Strip-plot designs are economically attractive in situations where the factors are hard to change and the process under investigation consists of two distinct stages, and where it is possible to apply the second stage to groups of semi-finished products from the first stage. They have a correlation structure similar to row-column designs and can be seen as special cases of split-lot designs. In this paper,we show how optimal design of experiments allows for the creation of a broad range of strip-plot designs.

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Bibliographic Info

Paper provided by University of Antwerp, Faculty of Applied Economics in its series Working Papers with number 2009007.

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Length: 21 pages
Date of creation: Jun 2009
Date of revision:
Handle: RePEc:ant:wpaper:2009007

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Postal: Prinsstraat 13, B-2000 Antwerpen
Web page: https://www.uantwerp.be/en/faculties/applied-economic-sciences/
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  1. Jones B. & GOOS, Peter, 2006. "A candidate-set-free algorithm for generating D-optimal split-plot designs," Working Papers 2006006, University of Antwerp, Faculty of Applied Economics.
  2. Eric Schoen, 1999. "Designing fractional two-level experiments with nested error structures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 495-508.
  3. Bradley Jones & Peter Goos, 2009. "D-optimal design of split-split-plot experiments," Biometrika, Biometrika Trust, vol. 96(1), pages 67-82.
  4. Goos, P. & Donev, A.N., 2006. "Blocking response surface designs," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1075-1088, November.
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