Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing
Mass personalized production in green manufacturing requires flexible adjustments in the assembly line, and the contradiction between the impact on resources and regaining production efficiency makes it difficult for decision-makers to balance. In order to quantify the cost of adjustment and provide...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180171 |
| _version_ | 1811691727076982784 |
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| author | Fan, Guoliang Zheng, Hao Jiang, Zuhua Liu, Jiangshan Lou, Shanhe |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Fan, Guoliang Zheng, Hao Jiang, Zuhua Liu, Jiangshan Lou, Shanhe |
| author_sort | Fan, Guoliang |
| collection | NTU |
| description | Mass personalized production in green manufacturing requires flexible adjustments in the assembly line, and the contradiction between the impact on resources and regaining production efficiency makes it difficult for decision-makers to balance. In order to quantify the cost of adjustment and provide a theoretical basis for rebalancing optimization, this paper introduces an adjustment complexity measurement method and a rebalancing optimization model. To solve the optimization problem, an adaptive rebalancing algorithm is designed based on the nondominated sorted genetic algorithm-II (NSGA-II). In this case, the algorithm is tailored to address the rebalancing problem by acquiring optimal operation distribution plans that minimize adjustment complexity. Finally, the effectiveness of the proposed rebalancing method is demonstrated through two case studies. Through analysis of algorithm performance and solutions, as well as comparison with existing algorithms, the results show that the proposed algorithm has good convergence and optimization performance. The proposed method can provide decision-makers with rebalancing solutions with different focuses. When adjusting the assembly line, the impact of the solution on resources and adjustment costs is fully considered. |
| first_indexed | 2024-10-01T06:24:29Z |
| format | Journal Article |
| id | ntu-10356/180171 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:24:29Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1801712024-09-23T02:15:11Z Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing Fan, Guoliang Zheng, Hao Jiang, Zuhua Liu, Jiangshan Lou, Shanhe School of Mechanical and Aerospace Engineering Engineering Assembly line Complexity measurement Mass personalized production in green manufacturing requires flexible adjustments in the assembly line, and the contradiction between the impact on resources and regaining production efficiency makes it difficult for decision-makers to balance. In order to quantify the cost of adjustment and provide a theoretical basis for rebalancing optimization, this paper introduces an adjustment complexity measurement method and a rebalancing optimization model. To solve the optimization problem, an adaptive rebalancing algorithm is designed based on the nondominated sorted genetic algorithm-II (NSGA-II). In this case, the algorithm is tailored to address the rebalancing problem by acquiring optimal operation distribution plans that minimize adjustment complexity. Finally, the effectiveness of the proposed rebalancing method is demonstrated through two case studies. Through analysis of algorithm performance and solutions, as well as comparison with existing algorithms, the results show that the proposed algorithm has good convergence and optimization performance. The proposed method can provide decision-makers with rebalancing solutions with different focuses. When adjusting the assembly line, the impact of the solution on resources and adjustment costs is fully considered. The work described in this paper is supported by a grant from the National Science Foundation of China (No. 72271163), China High-Tech Ship Project of the Ministry of Industry and Information Technology (No. [2019] 360). 2024-09-23T02:15:10Z 2024-09-23T02:15:10Z 2024 Journal Article Fan, G., Zheng, H., Jiang, Z., Liu, J. & Lou, S. (2024). Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing. Engineering Applications of Artificial Intelligence, 132, 107884-. https://dx.doi.org/10.1016/j.engappai.2024.107884 0952-1976 https://hdl.handle.net/10356/180171 10.1016/j.engappai.2024.107884 2-s2.0-85182906489 132 107884 en Engineering Applications of Artificial Intelligence © 2024 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering Assembly line Complexity measurement Fan, Guoliang Zheng, Hao Jiang, Zuhua Liu, Jiangshan Lou, Shanhe Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing |
| title | Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing |
| title_full | Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing |
| title_fullStr | Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing |
| title_full_unstemmed | Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing |
| title_short | Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing |
| title_sort | multi objective assembly line rebalancing problem based on complexity measurement in green manufacturing |
| topic | Engineering Assembly line Complexity measurement |
| url | https://hdl.handle.net/10356/180171 |
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