Performance evaluation of control chart for multiple assignable causes using genetic algorithm

Ahmed, I; Sultana, I; Paul, SK; Azeem, A

Performance evaluation of control chart for multiple assignable causes using genetic algorithm

Ahmed, I Sultana, I Paul, SK

Azeem, A

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Publication Type:: Journal Article
Citation:: International Journal of Advanced Manufacturing Technology, 2014, 70 (9-12), pp. 1889 - 1902
Issue Date:: 2014-02-01

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Field	Value	Language
dc.contributor.author	Ahmed, I	en_US
dc.contributor.author	Sultana, I	en_US
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Azeem, A	en_US
dc.date.issued	2014-02-01	en_US
dc.identifier.citation	International Journal of Advanced Manufacturing Technology, 2014, 70 (9-12), pp. 1889 - 1902	en_US
dc.identifier.issn	0268-3768	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118805
dc.description.abstract	With a view to monitoring and controlling manufacturing processes in industries, control charts are widely used and needed to be designed economically to achieve minimum quality costs. Many authors have studied the economic design of the X ̄ control chart after Duncan (J Am Stat Assoc 51(274):228-242, 1956) first proposed the economic model of the X ̄ control chart for a single assignable cause. But, in practice, multiple assignable causes are more logical and realistic. Moreover, the economic design does not consider statistical properties like bound on type I and type II error, and average time to signal (ATS). This paper focuses on evaluating the performance of genetic algorithm (GA) in pure economic and economic statistical design of the X ̄ control chart for multiple assignable causes. The performances of GA are demonstrated by comparing its result with the previously proposed grid search technique for a numerical example. The Duncan model of multiple assignable causes is adopted to formulate objective function, and the computation is achieved by approximation through a numerical method named Simpson's 1/3 rule. Comparison distinctly shows the superiority of GA over grid search results for economic statistical design. © 2013 Springer-Verlag London.	en_US
dc.relation.ispartof	International Journal of Advanced Manufacturing Technology	en_US
dc.relation.isbasedon	10.1007/s00170-013-5412-0	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Performance evaluation of control chart for multiple assignable causes using genetic algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	9-12	en_US
utslib.citation.volume	70	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	closed_access
pubs.issue	9-12	en_US
pubs.publication-status	Published	en_US
pubs.volume	70	en_US

Abstract:

With a view to monitoring and controlling manufacturing processes in industries, control charts are widely used and needed to be designed economically to achieve minimum quality costs. Many authors have studied the economic design of the X ̄ control chart after Duncan (J Am Stat Assoc 51(274):228-242, 1956) first proposed the economic model of the X ̄ control chart for a single assignable cause. But, in practice, multiple assignable causes are more logical and realistic. Moreover, the economic design does not consider statistical properties like bound on type I and type II error, and average time to signal (ATS). This paper focuses on evaluating the performance of genetic algorithm (GA) in pure economic and economic statistical design of the X ̄ control chart for multiple assignable causes. The performances of GA are demonstrated by comparing its result with the previously proposed grid search technique for a numerical example. The Duncan model of multiple assignable causes is adopted to formulate objective function, and the computation is achieved by approximation through a numerical method named Simpson's 1/3 rule. Comparison distinctly shows the superiority of GA over grid search results for economic statistical design. © 2013 Springer-Verlag London.

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http://hdl.handle.net/10453/118805