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Article: Using genetic algorithms to solve quality-related bin packing problem

TitleUsing genetic algorithms to solve quality-related bin packing problem
Authors
KeywordsBin packing
Genetic algorithms
Ion plating
Quality
Issue Date2007
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/rcim
Citation
Robotics And Computer-Integrated Manufacturing, 2007, v. 23 n. 1, p. 71-81 How to Cite?
AbstractThe Bin Packing Problem is an industrial problem which involves grouping items into appropriate bin to minimize the cost and number of used bins. It provides a solution for assigning parts to optimize some predefined measures of productivity. In this study, Ion Plating (IP) industry requires similar approach on allocating production jobs into batches for producing better quality products and enabling to meet customer deadlines. The aim of this paper is to (i) develop a Bin Packing Genetic Algorithms (BPGA) with different weighting combinations, taking into account the quality of product and service; (ii) improve the production efficiency by reducing the production unit cost in IP. Genetic Algorithm was chosen because it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company was used to simulate the proposed BPGA model, and the computational results were compared with these industrial data. The results from three different weighting combinations demonstrated that fewer resources would be required by applying the proposed model in solving BP problem in the Ion Plating Cell. © 2005 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/74513
ISSN
2015 Impact Factor: 2.077
2015 SCImago Journal Rankings: 1.621
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, FTSen_HK
dc.contributor.authorAu, KCen_HK
dc.contributor.authorChan, LYen_HK
dc.contributor.authorLau, TLen_HK
dc.date.accessioned2010-09-06T07:02:04Z-
dc.date.available2010-09-06T07:02:04Z-
dc.date.issued2007en_HK
dc.identifier.citationRobotics And Computer-Integrated Manufacturing, 2007, v. 23 n. 1, p. 71-81en_HK
dc.identifier.issn0736-5845en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74513-
dc.description.abstractThe Bin Packing Problem is an industrial problem which involves grouping items into appropriate bin to minimize the cost and number of used bins. It provides a solution for assigning parts to optimize some predefined measures of productivity. In this study, Ion Plating (IP) industry requires similar approach on allocating production jobs into batches for producing better quality products and enabling to meet customer deadlines. The aim of this paper is to (i) develop a Bin Packing Genetic Algorithms (BPGA) with different weighting combinations, taking into account the quality of product and service; (ii) improve the production efficiency by reducing the production unit cost in IP. Genetic Algorithm was chosen because it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company was used to simulate the proposed BPGA model, and the computational results were compared with these industrial data. The results from three different weighting combinations demonstrated that fewer resources would be required by applying the proposed model in solving BP problem in the Ion Plating Cell. © 2005 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/rcimen_HK
dc.relation.ispartofRobotics and Computer-Integrated Manufacturingen_HK
dc.subjectBin packingen_HK
dc.subjectGenetic algorithmsen_HK
dc.subjectIon platingen_HK
dc.subjectQualityen_HK
dc.titleUsing genetic algorithms to solve quality-related bin packing problemen_HK
dc.typeArticleen_HK
dc.identifier.emailChan, FTS: ftschan@hkucc.hku.hken_HK
dc.identifier.emailChan, LY: plychan@hku.hken_HK
dc.identifier.emailLau, TL: tllau@hkucc.hku.hken_HK
dc.identifier.authorityChan, FTS=rp00090en_HK
dc.identifier.authorityChan, LY=rp00093en_HK
dc.identifier.authorityLau, TL=rp00138en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rcim.2005.11.001en_HK
dc.identifier.scopuseid_2-s2.0-33750692015en_HK
dc.identifier.hkuros136091en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33750692015&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_HK
dc.identifier.issue1en_HK
dc.identifier.spage71en_HK
dc.identifier.epage81en_HK
dc.identifier.isiWOS:000242697800007-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, FTS=7202586517en_HK
dc.identifier.scopusauthoridAu, KC=8215393200en_HK
dc.identifier.scopusauthoridChan, LY=7403540482en_HK
dc.identifier.scopusauthoridLau, TL=7102222436en_HK

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