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A multi-objective evolutionary algorithm-based decision support system: a case study on job-shop scheduling in manufacturing

Tan, Choo Jun, Hanoun, Samer, Lim, Chee Peng, Creighton, Douglas and Nahavandi, Saeid 2015, A multi-objective evolutionary algorithm-based decision support system: a case study on job-shop scheduling in manufacturing, in SysCon 2015: Proceedings of the 9th Annual IEEE International Systems Conference, IEEE, Piscataway, N.J., pp. 170-174, doi: 10.1109/SYSCON.2015.7116747.

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Title A multi-objective evolutionary algorithm-based decision support system: a case study on job-shop scheduling in manufacturing
Author(s) Tan, Choo Jun
Hanoun, Samer
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE International Systems Conference (9th: 2015: Vancouver, B.C.)
Conference location Vancouver, B.C.
Conference dates 13-16 Apr. 2015
Title of proceedings SysCon 2015: Proceedings of the 9th Annual IEEE International Systems Conference
Publication date 2015
Start page 170
End page 174
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Summary In this paper, an evolutionary algorithm is used for developing a decision support tool to undertake multi-objective job-shop scheduling problems. A modified micro genetic algorithm (MmGA) is adopted to provide optimal solutions according to the Pareto optimality principle in solving multi-objective optimisation problems. MmGA operates with a very small population size to explore a wide search space of function evaluations and to improve the convergence score towards the true Pareto optimal front. To evaluate the effectiveness of the MmGA-based decision support tool, a multi-objective job-shop scheduling problem with actual information from a manufacturing company is deployed. The statistical bootstrap method is used to evaluate the experimental results, and compared with those from the enumeration method. The outcome indicates that the decision support tool is able to achieve those optimal solutions as generated by the enumeration method. In addition, the proposed decision support tool has advantage of achieving the results within a fraction of the time.
ISBN 9781479959273
Language eng
DOI 10.1109/SYSCON.2015.7116747
Field of Research 010303 Optimisation
091099 Manufacturing Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079040

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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