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Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
journal contribution
posted on 2019-02-01, 00:00 authored by C J Tan, S C Neoh, Chee Peng LimChee Peng Lim, Samer HanounSamer Hanoun, W P Wong, C K Loo, L Zhang, Saeid NahavandiIn this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.
History
Journal
Journal of intelligent manufacturingVolume
30Issue
2Pagination
879 - 890Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
0956-5515eISSN
1572-8145Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2017, SpringerUsage metrics
Keywords
Multi-objective optimisationEvolutionary algorithmEnsemble modelJob-shop schedulingScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, ManufacturingComputer ScienceEngineeringMULTIOBJECTIVE GENETIC ALGORITHMOPTIMIZATIONFLOWSHOPFRAMEWORKPARAMETERSOPTIMALITYMULTIPLESUPPORTSEARCHDESIGNArtificial Intelligence and Image Processing
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