Journal of Intelligent Manufacturing

, Volume 30, Issue 2, pp 879–890 | Cite as

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

  • Choo Jun Tan
  • Siew Chin Neoh
  • Chee Peng LimEmail author
  • Samer Hanoun
  • Wai Peng Wong
  • Chu Kong Loo
  • Li Zhang
  • Saeid Nahavandi


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.


Multi-objective optimisation Evolutionary algorithm Ensemble model Job-shop scheduling 



The financial support of Collaborative Research in Engineering, Science and Technology (CREST) (Grant No. P05C2-14) is highly appreciated.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Choo Jun Tan
    • 1
  • Siew Chin Neoh
    • 2
  • Chee Peng Lim
    • 3
    Email author
  • Samer Hanoun
    • 3
  • Wai Peng Wong
    • 4
  • Chu Kong Loo
    • 5
  • Li Zhang
    • 6
  • Saeid Nahavandi
    • 3
  1. 1.School of Science and TechnologyWawasan Open UniversityGeorge TownMalaysia
  2. 2.Faculty of Engineering, Technology and Built EnvironmentUCSI UniversityKuala LumpurMalaysia
  3. 3.Institute for Intelligent Systems Research and InnovationDeakin UniversityGeelongAustralia
  4. 4.School of ManagementUniversity Science of MalaysiaGeorge TownMalaysia
  5. 5.Department of Artificial Intelligence, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  6. 6.Department of Computer Science and Digital Technologies, Faculty of Engineering and EnvironmentNorthumbria UniversityNewcastle upon TyneUnited Kingdom

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