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Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Authors
Liu, Si-ChenChen, Zong-GanZhan, Zhi-HuiJeon, Sang-WoonKwong, SamZhang, Jun
Issue Date
Mar-2023
Publisher
IEEE Advancing Technology for Humanity
Keywords
Optimization; Statistics; Sociology; Costs; Genetic algorithms; Production facilities; Job shop scheduling; Archive sharing technique (AST); archive update strategy (AUS); genetic algorithm (GA); many-objective job-shop scheduling problem (MaJSSP); many-objective optimization; multiple populations for multiple objectives (MPMO)
Citation
IEEE Transactions on Cybernetics, v.53, no.3, pp 1460 - 1474
Pages
15
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Cybernetics
Volume
53
Number
3
Start Page
1460
End Page
1474
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111656
DOI
10.1109/TCYB.2021.3102642
ISSN
2168-2267
2168-2275
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.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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