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

• Published in: IEEE transactions on cybernetics Vol. 53; no. 3; pp. 1460 - 1474
• Main Authors: Liu, Si-Chen, Chen, Zong-Gan, Zhan, Zhi-Hui, Jeon, Sang-Woon, Kwong, Sam, Zhang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Archive sharing technique (AST); archive update strategy (AUS); Archives & records; Completion time; Costs; Factories; genetic algorithm (GA); Genetic algorithms; Industrial engineering; Job shop scheduling; Manufacturing engineering; many-objective job-shop scheduling problem (MaJSSP); many-objective optimization; Multiple objective analysis; multiple populations for multiple objectives (MPMO); Objectives; Operating costs; Optimization; Populations; Production costs; Production facilities; Sociology; Statistics
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2021.3102642

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.