Application of an evolutionary algorithm-based ensemble model to job-shop scheduling Tan, Choo Jun, Neoh, Siew Chin, Lim, Chee Peng, Hanoun, Samer, Wong, Wai Peng, Loo, Chu Kong, Zhang, Li and Nahavandi, Saeid 2017, Application of an evolutionary algorithm-based ensemble model to job-shop scheduling, Journal of intelligent manufacturing, pp. 1-12, doi: 10.1007/s10845-016-1291-1. Attached Files Name Description MIMEType Size Downloads Title Application of an evolutionary algorithm-based ensemble model to job-shop scheduling Author(s) Tan, Choo Jun Neoh, Siew Chin Lim, Chee Peng orcid.org/0000-0003-4191-9083 Hanoun, Samer orcid.org/0000-0002-8697-1515 Wong, Wai Peng Loo, Chu Kong Zhang, Li Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Journal of intelligent manufacturing Start page 1 End page 12 Total pages 12 Publisher Springer Place of publication Berlin, Germany Publication date 2017-01-05 ISSN 0956-5515 1572-8145 Keyword(s) Multi-objective optimisation Evolutionary algorithm Ensemble model Job-shop scheduling Summary In 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. Notes In Press Language eng DOI 10.1007/s10845-016-1291-1 Field of Research 099999 Engineering not elsewhere classified 0910 Manufacturing Engineering 0801 Artificial Intelligence And Image Processing 0899 Other Information And Computing Sciences Socio Economic Objective 970110 Expanding Knowledge in Technology HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2016, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30090877 Document type: Journal Article Collection: Centre for Intelligent Systems Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 30 Jan 2017, 11:30:47 EST Tue, 31 Jan 2017, 04:00:51 EST Tue, 31 Jan 2017, 07:14:35 EST Fri, 21 Apr 2017, 13:50:05 EST Tue, 06 Jun 2017, 06:04:54 EST Wed, 07 Jun 2017, 11:11:44 EST Wed, 07 Jun 2017, 11:14:02 EST Wed, 07 Jun 2017, 11:14:46 EST Wed, 07 Jun 2017, 11:40:39 EST Fri, 22 Sep 2017, 12:45:20 EST Mon, 25 Sep 2017, 07:55:14 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 108 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 30 Jan 2017, 11:30:47 EST

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