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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
20 Downloads (Pure)

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 languageEnglish
Pages (from-to)879-890
JournalJournal of Intelligent Manufacturing
Volume30
Issue number2
Early online date5 Jan 2017
DOIs
Publication statusPublished - 1 Feb 2019

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