Manufacturing engineering production line scheduling management technology integrating availability constraints and heuristic rules
With the rapid development of manufacturing technology, scheduling and management of engineering production lines are becoming increasingly important. However, the current manufacturing engineering production line scheduling and management technology often has problems with low quality. To improve q...
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| Format: | Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
De Gruyter
2025-06-01
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| Schriftenreihe: | Nonlinear Engineering |
| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.1515/nleng-2025-0119 |
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| Zusammenfassung: | With the rapid development of manufacturing technology, scheduling and management of engineering production lines are becoming increasingly important. However, the current manufacturing engineering production line scheduling and management technology often has problems with low quality. To improve quality, this study proposes a scheduling management model that combines availability constraints and outsourcing. To solve this model, a hybrid algorithm based on heuristic rules and the Johnson-Bellman rule is also constructed. In comparing the performance of heuristic algorithms with other algorithms, the optimization rates of heuristic algorithms with SHPSO, QLINSGA-II, and Q-Learning-Sarsa-K-mes-GA were 96.7, 90, 78.6, and 84.7%, respectively. The average processing time was 998.7, 6287.3, 6698.9, and 6986.8 h, respectively. Among them, the proposed heuristic algorithm had the highest optimization rate and the shortest average processing time, which were significantly better than the compared algorithms. In addition, in the comparative analysis of the established scheduling management model, the average processing time of the model compared to SHPSO, QLINSGA-II, and Q-Learning-Sarsa-k-mean-GA was 196.7, 396.8, 226.7, and 498.2 h. The average processing costs were 1456.7 yuan, 3897.4 yuan, 2346.1 yuan, and 4968.6 yuan. Among them, the average processing time and average processing cost of this model were the lowest, which performed better than the comparative models. The above results indicate that the proposed model and hybrid algorithm have good performance and effectiveness, which can help improve the quality of engineering production line scheduling management. |
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| ISSN: | 2192-8029 |