Many-Objective Job-Shop Scheduling : A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Pages (from-to) | 1460-1474 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 53 |
Issue number | 3 |
Online published | 13 Sept 2021 |
Publication status | Published - Mar 2023 |
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DOI | DOI |
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Attachment(s) | Documents
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85115155797&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(8f73c547-ff2d-4033-a46e-78baaeb70311).html |
Abstract
The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) framework-based genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy (AUS) is proposed to further improve the quality of the solutions in the archive. The test instances in the widely used test sets are adopted to evaluate the performance of MPMOGA. The experimental results show that MPMOGA outperforms the compared state-of-the-art algorithms on most of the test instances.
Research Area(s)
- Archive sharing technique (AST), archive update strategy (AUS), Costs, genetic algorithm (GA), Genetic algorithms, Job shop scheduling, many-objective job-shop scheduling problem (MaJSSP), many-objective optimization, multiple populations for multiple objectives (MPMO)., Optimization, Production facilities, Sociology, Statistics
Citation Format(s)
Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach. / Liu, Si-Chen; Chen, Zong-Gan; Zhan, Zhi-Hui et al.
In: IEEE Transactions on Cybernetics, Vol. 53, No. 3, 03.2023, p. 1460-1474.
In: IEEE Transactions on Cybernetics, Vol. 53, No. 3, 03.2023, p. 1460-1474.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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