An effective algorithm for generation of factorial designs with generalized minimum aberration

Kai Tai Fang; Aijun Zhang; Runze Li

doi:10.1016/j.jco.2007.03.010

An effective algorithm for generation of factorial designs with generalized minimum aberration

Kai Tai Fang, Aijun Zhang, Runze Li

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Fractional factorial designs are popular and widely used for industrial experiments. Generalized minimum aberration is an important criterion recently proposed for both regular and non-regular designs. This paper provides a formal optimization treatment on optimal designs with generalized minimum aberration. New lower bounds and optimality results are developed for resolution-III designs. Based on these results, an effective computer search algorithm is provided for sub-design selection, and new optimal designs are reported.

Original language	English (US)
Pages (from-to)	740-751
Number of pages	12
Journal	Journal of Complexity
Volume	23
Issue number	4-6
DOIs	https://doi.org/10.1016/j.jco.2007.03.010
State	Published - 2007

All Science Journal Classification (ASJC) codes

Algebra and Number Theory
Statistics and Probability
Numerical Analysis
Mathematics(all)
Control and Optimization
Applied Mathematics

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10.1016/j.jco.2007.03.010

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title = "An effective algorithm for generation of factorial designs with generalized minimum aberration",

abstract = "Fractional factorial designs are popular and widely used for industrial experiments. Generalized minimum aberration is an important criterion recently proposed for both regular and non-regular designs. This paper provides a formal optimization treatment on optimal designs with generalized minimum aberration. New lower bounds and optimality results are developed for resolution-III designs. Based on these results, an effective computer search algorithm is provided for sub-design selection, and new optimal designs are reported.",

author = "Fang, {Kai Tai} and Aijun Zhang and Runze Li",

note = "Funding Information: The authors thank the Associate Editor and two anonymous referees for their valuable comments. Fang and Zhang were partially supported by the Hong Kong RGC-Grant RGC/HKBU 200804. Zhang was also supported by University of Michigan and thanks Professor Vijay Nair for valuable suggestions. Li was supported by a National Institute on Drug Abuse (NIDA) Grant P50 DA10075, and partially supported by NSF Grants DMS 0348869 and CCF 0430349.",

year = "2007",

doi = "10.1016/j.jco.2007.03.010",

language = "English (US)",

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T1 - An effective algorithm for generation of factorial designs with generalized minimum aberration

AU - Fang, Kai Tai

AU - Zhang, Aijun

AU - Li, Runze

N1 - Funding Information: The authors thank the Associate Editor and two anonymous referees for their valuable comments. Fang and Zhang were partially supported by the Hong Kong RGC-Grant RGC/HKBU 200804. Zhang was also supported by University of Michigan and thanks Professor Vijay Nair for valuable suggestions. Li was supported by a National Institute on Drug Abuse (NIDA) Grant P50 DA10075, and partially supported by NSF Grants DMS 0348869 and CCF 0430349.

PY - 2007

Y1 - 2007

N2 - Fractional factorial designs are popular and widely used for industrial experiments. Generalized minimum aberration is an important criterion recently proposed for both regular and non-regular designs. This paper provides a formal optimization treatment on optimal designs with generalized minimum aberration. New lower bounds and optimality results are developed for resolution-III designs. Based on these results, an effective computer search algorithm is provided for sub-design selection, and new optimal designs are reported.

AB - Fractional factorial designs are popular and widely used for industrial experiments. Generalized minimum aberration is an important criterion recently proposed for both regular and non-regular designs. This paper provides a formal optimization treatment on optimal designs with generalized minimum aberration. New lower bounds and optimality results are developed for resolution-III designs. Based on these results, an effective computer search algorithm is provided for sub-design selection, and new optimal designs are reported.

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JO - Journal of Complexity

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ER -

An effective algorithm for generation of factorial designs with generalized minimum aberration

Abstract

All Science Journal Classification (ASJC) codes

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