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Operations Research, Systems Engineering and Industrial Engineering Commons

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Faculty Publications

Sequential design

Publication Year

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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

A General Intuitive Design Pattern For Optimally Sequencing Treatment Combinations In 2k Factorial Experiment And A Simple Estimation Algorithm, H.-S. Jacob Tsao, Minnie Patel Jan 2015

A General Intuitive Design Pattern For Optimally Sequencing Treatment Combinations In 2k Factorial Experiment And A Simple Estimation Algorithm, H.-S. Jacob Tsao, Minnie Patel

Faculty Publications

The number of model parameters of a 2k factorial design grows exponentially. When the number of factors is large, numerous higher-order interactions constitute a vast majority of the model parameters while many of them do not exist or are insignificant. The classic methods of fractional factorial designs, Plackett–Burman designs, Taguchi designs, etc. seek an already developed and often cataloged design that fits exactly the problem being tackled or select a design that fits it the most. Most, if not all, of these designs were developed in absence of convenient computation tools and enjoy computational simplicity. The necessary number of treatment …


An Intuitive Design Pattern For Sequentially Estimating Parameters Of A 2k Factorial Experiment With Active Confounding Avoidance And Least Treatment Combinations, H.-S. Jacob Tsao, Minnie Patel Jan 2013

An Intuitive Design Pattern For Sequentially Estimating Parameters Of A 2k Factorial Experiment With Active Confounding Avoidance And Least Treatment Combinations, H.-S. Jacob Tsao, Minnie Patel

Faculty Publications

2k Full factorial designs may be prohibitively expensive when the number of factors k is large. The most popular technique developed to reduce the number of treatment combinations is the fractional factorial design; confounding in estimating the model parameters naturally results in various resolution and aberration levels. While very useful, these resolution levels may not satisfy experimenters’ requirements for estimatibility and cost reduction. For example, while Resolution V ensures a common requirement that no two-factor interactions are confounded, it also imposes an often undesired restriction that a main effect cannot be confounded with a three-factor interaction, which may very well …