Neural network simulation metamodels and genetic algorithms in analysis and design of manufacturing cells

Vosniakos, GC; Tsifakis, A; Benardos, P

dc.contributor.author	Vosniakos, GC	en
dc.contributor.author	Tsifakis, A	en
dc.contributor.author	Benardos, P	en
dc.date.accessioned	2014-03-01T01:24:42Z
dc.date.available	2014-03-01T01:24:42Z
dc.date.issued	2006	en
dc.identifier.issn	0268-3768	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17404
dc.subject	Artificial neural networks	en
dc.subject	discrete event simulation	en
dc.subject	genetic algorithms	en
dc.subject	manufacturing cells	en
dc.subject	metamodels	en
dc.subject	operations optimisation	en
dc.subject.classification	Automation & Control Systems	en
dc.subject.classification	Engineering, Manufacturing	en
dc.subject.other	SYSTEMS	en
dc.subject.other	SEARCH	en
dc.subject.other	OPTIMIZATION	en
dc.subject.other	INTEGRATION	en
dc.subject.other	HYBRID	en
dc.title	Neural network simulation metamodels and genetic algorithms in analysis and design of manufacturing cells	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00170-005-2535-y	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00170-005-2535-y	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	A manufacturing cell can be modelled using discrete event simulation in order to predict its performance under various combinations of input parameters, related to design issues as well as operation issues. Such models suffer from inherent lack of generality and are useful on a case-by-case basis. Therefore, determination of the best values of design and operation parameters in combination cannot be performed rigorously, but only on an experimentation basis. This work proposes a systematic procedure for optimisation. The final stage is an optimisation procedure using a genetic algorithm which uses the classic genetic operators tuned with due care to avoid local maxima of the fitness function. The actual value of the fitness function corresponding to each breeding case could be obtained by running the discrete event simulation application, but that would make for lengthy response. Therefore, a first stage of the optimising procedure is proposed that calculates the essential part of the fitness function by a neural network metamodel generalising on simulation results. Conditions for successful application of the above procedures are discussed.	en
heal.publisher	SPRINGER LONDON LTD	en
heal.journalName	INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY	en
dc.identifier.doi	10.1007/s00170-005-2535-y	en
dc.identifier.isi	ISI:000238835400015	en
dc.identifier.volume	29	en
dc.identifier.issue	5	en
dc.identifier.spage	541	en
dc.identifier.epage	550	en