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

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Abstract

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.

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[adopted from Coello and Pulido (2005)]

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Acknowledgements

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

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Authors and Affiliations

  1. School of Science and Technology, Wawasan Open University, George Town, Malaysia

    Choo Jun Tan

  2. Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia

    Siew Chin Neoh

  3. Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, Australia

    Chee Peng Lim, Samer Hanoun & Saeid Nahavandi

  4. School of Management, University Science of Malaysia, George Town, Malaysia

    Wai Peng Wong

  5. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Chu Kong Loo

  6. Department of Computer Science and Digital Technologies, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, United Kingdom

    Li Zhang

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  1. Choo Jun Tan
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Correspondence to Chee Peng Lim.

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This research is supported by Collaborative Research in Engineering, Science & Technology (CREST) Grant P05C2-14.

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Tan, C.J., Neoh, S.C., Lim, C.P. et al. Application of an evolutionary algorithm-based ensemble model to job-shop scheduling. J Intell Manuf 30, 879–890 (2019). https://doi.org/10.1007/s10845-016-1291-1

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