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A constraint programming-based genetic algorithm (CPGA) for capacity output optimization
dc.contributor.author | Goh, Kate Ean Nee |
dc.contributor.author | Chin, Jeng Feng |
dc.contributor.author | Loh, Wei Ping |
dc.contributor.author | Tan, Melissa Chea-Ling |
dc.date.accessioned | 2015-04-13T15:17:01Z |
dc.date.available | 2015-04-13T15:17:01Z |
dc.date.issued | 2014-12 |
dc.identifier.citation | Goh, Kate Ean Nee; Chin, Jeng Feng; Loh, Wei Ping. A constraint programming-based genetic algorithm (CPGA) for capacity output optimization. "Journal of Industrial Engineering and Management", Desembre 2014, vol. 7, núm. 5, p. 1222-1249. |
dc.identifier.issn | 2013-0953 |
dc.identifier.uri | http://hdl.handle.net/2099/16316 |
dc.description.abstract | Purpose: The manuscript presents an investigation into a constraint programming-based genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing company. Design/methodology/approach: In the first stage, constraint programming defining the relationships between variables was formulated into the objective function. A genetic algorithm model was created in the second stage to optimize capacity output. Three demand scenarios were applied to test the robustness of the proposed algorithm. Findings: CPGA improved both the machine utilization and capacity output once the minimum requirements of a demand scenario were fulfilled. Capacity outputs of the three scenarios were improved by 157%, 7%, and 69%, respectively. Research limitations/implications: The work relates to aggregate planning of machine capacity in a single case study. The constraints and constructed scenarios were therefore industry-specific.Practical implications: Capacity planning in a semiconductor manufacturing facility need to consider multiple mutually influenced constraints in resource availability, process flow and product demand. The findings prove that CPGA is a practical and an efficient alternative to optimize the capacity output and to allow the company to review its capacity with quick feedback. Originality/value: The work integrates two contemporary computational methods for a real industry application conventionally reliant on human judgement. |
dc.format.extent | 28 p. |
dc.language.iso | eng |
dc.publisher | OmniaScience |
dc.rights | Attribution-NonCommercial 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d’operacions |
dc.subject.lcsh | Computer algorithms |
dc.subject.lcsh | Production planning |
dc.subject.lcsh | Constraint programming (Computer science) |
dc.subject.other | Semiconductor capacity management |
dc.subject.other | Production planning |
dc.subject.other | Constraint programming |
dc.subject.other | Genetic algorithm |
dc.title | A constraint programming-based genetic algorithm (CPGA) for capacity output optimization |
dc.type | Article |
dc.subject.lemac | Algorismes genètics |
dc.subject.lemac | Producció -- Planificació |
dc.subject.lemac | Programació per restriccions (Informàtica) |
dc.identifier.dl | B-28744-2008 |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Open Access |
local.citation.author | Goh, Kate Ean Nee; Chin, Jeng Feng; Loh, Wei Ping |
local.citation.publicationName | Journal of Industrial Engineering and Management |
local.citation.volume | 7 |
local.citation.number | 5 |
local.citation.startingPage | 1222 |
local.citation.endingPage | 1249 |
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2014, vol. 7, núm. 5 [26]