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

Version 2 2024-06-06, 08:07
Version 1 2017-01-30, 12:30
journal contribution
posted on 2024-06-06, 08:07 authored by CJ Tan, SC Neoh, Chee Peng LimChee Peng Lim, Samer HanounSamer Hanoun, WP Wong, CK Loo, L Zhang, S 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.

History

Journal

Journal of intelligent manufacturing

Volume

30

Pagination

879-890

Location

Berlin, Germany

ISSN

0956-5515

eISSN

1572-8145

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2017, Springer

Issue

2

Publisher

Springer