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

Choo Jun Tan, Siew Chin Neoh, Chee Peng Lim (), Samer Hanoun, Wai Peng Wong, Chu Kong Loo, Li Zhang and Saeid Nahavandi
Additional contact information
Choo Jun Tan: Wawasan Open University
Siew Chin Neoh: UCSI University
Chee Peng Lim: Deakin University
Samer Hanoun: Deakin University
Wai Peng Wong: University Science of Malaysia
Chu Kong Loo: University of Malaya
Li Zhang: Northumbria University
Saeid Nahavandi: Deakin University

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 2, No 27, 879-890

Abstract: 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.

Keywords: Multi-objective optimisation; Evolutionary algorithm; Ensemble model; Job-shop scheduling (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-016-1291-1

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