File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A genetic algorithm approach to bin packing in an ion plating cell

TitleA genetic algorithm approach to bin packing in an ion plating cell
Authors
KeywordsBin packing
Genetic algorithms
Ion plating
Quality
Issue Date2005
PublisherProfessional Engineering Publishing Ltd. The Journal's web site is located at http://journals.pepublishing.com/link.asp?id=119784
Citation
Proceedings Of The Institution Of Mechanical Engineers, Part B: Journal Of Engineering Manufacture, 2005, v. 219 n. 1, p. 1-13 How to Cite?
AbstractA bin packing (BP) problem is an industrial problem that arises when grouping items into appropriate bins to minimize cost and the number of bins used. In this study, the ion plating industry requires a similar approach when allocating production jobs into batches for producing better-quality products and enabling deadlines to be met so that the customer is satisfied. The aim of this paper is to develop a BP model based on genetic algorithms (GAs) to (a) introduce quality of product and service into bin packing problems and (b) improve the production efficiency by reducing the production unit cost in ion plating. A GA is chosen since it is one of the best heuristic algorithms for solving optimization problems. In the case study, industrial data of a precious-metal finishing company have been input into the proposed bin packing genetic algorithm (BPGA) model, and the computational results have been compared with these industrial data. The results demonstrated that less resource would be required by applying the proposed model in solving a BP problem in the ion plating cell.
Persistent Identifierhttp://hdl.handle.net/10722/74539
ISSN
2015 Impact Factor: 0.978
2015 SCImago Journal Rankings: 0.541
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, FTSen_HK
dc.contributor.authorAu, KCen_HK
dc.contributor.authorChan, PLYen_HK
dc.date.accessioned2010-09-06T07:02:20Z-
dc.date.available2010-09-06T07:02:20Z-
dc.date.issued2005en_HK
dc.identifier.citationProceedings Of The Institution Of Mechanical Engineers, Part B: Journal Of Engineering Manufacture, 2005, v. 219 n. 1, p. 1-13en_HK
dc.identifier.issn0954-4054en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74539-
dc.description.abstractA bin packing (BP) problem is an industrial problem that arises when grouping items into appropriate bins to minimize cost and the number of bins used. In this study, the ion plating industry requires a similar approach when allocating production jobs into batches for producing better-quality products and enabling deadlines to be met so that the customer is satisfied. The aim of this paper is to develop a BP model based on genetic algorithms (GAs) to (a) introduce quality of product and service into bin packing problems and (b) improve the production efficiency by reducing the production unit cost in ion plating. A GA is chosen since it is one of the best heuristic algorithms for solving optimization problems. In the case study, industrial data of a precious-metal finishing company have been input into the proposed bin packing genetic algorithm (BPGA) model, and the computational results have been compared with these industrial data. The results demonstrated that less resource would be required by applying the proposed model in solving a BP problem in the ion plating cell.en_HK
dc.languageengen_HK
dc.publisherProfessional Engineering Publishing Ltd. The Journal's web site is located at http://journals.pepublishing.com/link.asp?id=119784en_HK
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufactureen_HK
dc.subjectBin packingen_HK
dc.subjectGenetic algorithmsen_HK
dc.subjectIon platingen_HK
dc.subjectQualityen_HK
dc.titleA genetic algorithm approach to bin packing in an ion plating cellen_HK
dc.typeArticleen_HK
dc.identifier.emailChan, FTS: ftschan@hkucc.hku.hken_HK
dc.identifier.emailChan, PLY: plychan@hku.hken_HK
dc.identifier.authorityChan, FTS=rp00090en_HK
dc.identifier.authorityChan, PLY=rp00093en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1243/095440505X8019en_HK
dc.identifier.scopuseid_2-s2.0-12844283930en_HK
dc.identifier.hkuros100312en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-12844283930&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume219en_HK
dc.identifier.issue1en_HK
dc.identifier.spage1en_HK
dc.identifier.epage13en_HK
dc.identifier.isiWOS:000228764300001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, FTS=7202586517en_HK
dc.identifier.scopusauthoridAu, KC=8215393200en_HK
dc.identifier.scopusauthoridChan, PLY=7403540482en_HK
dc.identifier.citeulike76432-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats