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; Saeid Nahavandi

doi:10.1007/s10845-016-1291-1

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, Saeid Nahavandi

Computer and Information Sciences Department

Research output: Contribution to journal › Article › peer-review

24 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	879-890
Number of pages	12
Journal	Journal of Intelligent Manufacturing
Volume	30
Issue number	2
Early online date	5 Jan 2017
DOIs	https://doi.org/10.1007/s10845-016-1291-1
Publication status	Published - 15 Feb 2019

Keywords

Multi-objective optimisation
Evolutionary algorithm
Ensemble model
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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AU - Tan, Choo Jun

AU - Neoh, Siew Chin

AU - Lim, Chee Peng

AU - Hanoun, Samer

AU - Wong, Wai Peng

AU - Loo, Chu Kong

AU - Zhang, Li

AU - Nahavandi, Saeid

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AB - 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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KW - Evolutionary algorithm

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

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