An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop
This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, includin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2023-01-01
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| Series: | International Journal of Industrial Engineering Computations |
| Online Access: | http://www.growingscience.com/ijiec/Vol14/IJIEC_2023_27.pdf |
| _version_ | 1797683967609012224 |
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| author | Yuan-Zhuang Li Jia-Zhen Zou Yang-Li Jia Lei-Lei Meng Wen-Qiang Zou |
| author_facet | Yuan-Zhuang Li Jia-Zhen Zou Yang-Li Jia Lei-Lei Meng Wen-Qiang Zou |
| author_sort | Yuan-Zhuang Li |
| collection | DOAJ |
| description | This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature. |
| first_indexed | 2024-03-12T00:23:32Z |
| format | Article |
| id | doaj.art-e202dc35e0984df4824c1111d9fece5f |
| institution | Directory Open Access Journal |
| issn | 1923-2926 1923-2934 |
| language | English |
| last_indexed | 2024-03-12T00:23:32Z |
| publishDate | 2023-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Industrial Engineering Computations |
| spelling | doaj.art-e202dc35e0984df4824c1111d9fece5f2023-09-15T12:02:20ZengGrowing ScienceInternational Journal of Industrial Engineering Computations1923-29261923-29342023-01-0114476778410.5267/j.ijiec.2023.7.002An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshopYuan-Zhuang Li Jia-Zhen Zou Yang-Li JiaLei-Lei MengWen-Qiang Zou This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature.http://www.growingscience.com/ijiec/Vol14/IJIEC_2023_27.pdf |
| spellingShingle | Yuan-Zhuang Li Jia-Zhen Zou Yang-Li Jia Lei-Lei Meng Wen-Qiang Zou An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop International Journal of Industrial Engineering Computations |
| title | An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop |
| title_full | An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop |
| title_fullStr | An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop |
| title_full_unstemmed | An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop |
| title_short | An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop |
| title_sort | improved genetic algorithm for multi agv dispatching problem with unloading setup time in a matrix manufacturing workshop |
| url | http://www.growingscience.com/ijiec/Vol14/IJIEC_2023_27.pdf |
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