[1]WANG Yufang,ZHANG Yi,YAO Binbin,et al.Flexible job shop scheduling considering transportation and machine pre-maintenance[J].CAAI Transactions on Intelligent Systems,2025,20(3):707-718.[doi:10.11992/tis.202405020]
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Flexible job shop scheduling considering transportation and machine pre-maintenance

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 707-718 Column: 学术论文—智能系统 Public date: 2025-05-05
Title:
Flexible job shop scheduling considering transportation and machine pre-maintenance
Author(s):
WANG Yufang1; 2; 3;  ZHANG Yi1;  YAO Binbin1;  CHEN Fan1;  GE Shiyu1
1. School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China;
3. Engineering Research Center on Meteorological Energy Using and Control (C-MEIC), Nanjing University of Information Science & Technology, Nanjing 210044, China
Keywords:
transportation constraint; pre-maintenance; aerospace manufacturing; NSGA-II; population quality; group evolution; heuristic initialization; local search
CLC:
TP18；TH165
DOI:
10.11992/tis.202405020
Abstract:
The rapid development of the aviation manufacturing industry has increased the demand for high-efficiency and low-energy modes of consumption and production. Here, a modeling and analysis approach was employed to address the green scheduling issues of an aerospace flexible job shop regarding transportation and pre-maintenance. Additionally, a model was established to minimize the completion time, bottleneck machine workload, and total energy consumption. Further, non-dominated sorting genetic algorithm II based on population quality was proposed to resolve the issues. Furthermore, heuristic initialization was employed to generate high-quality initial populations, and individuals were grouped to evolve. Thereafter, local search operations were conducted to comprehensively explore the optimal individuals. For the populations classified as moderate, crossover and mutation combined with machine load operations were applied to alter portions of their genetic makeup and achieve optimal solutions. Employing a learning mechanism, the inferior populations acquired superior genes from elite individuals to enhance their overall quality. Subsequently, the effectiveness of the proposed algorithm was verified by comparing it with those of other algorithms on test examples. Finally, the algorithm was applied to a real aerospace composite material manufacturing system to schedule actual production activities, thereby verifying its feasibility.
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