A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm
The reasonable scheduling of agricultural machinery can avoid their purposeless flow during the operational service and reduce the scheduling cost of agricultural machinery service centers. In this research, a multiregional agricultural machinery scheduling model with a time window was established c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/5/1042 |
| _version_ | 1797601472772308992 |
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| author | Huang Huang Xinwei Cuan Zhuo Chen Lina Zhang Hao Chen |
| author_facet | Huang Huang Xinwei Cuan Zhuo Chen Lina Zhang Hao Chen |
| author_sort | Huang Huang |
| collection | DOAJ |
| description | The reasonable scheduling of agricultural machinery can avoid their purposeless flow during the operational service and reduce the scheduling cost of agricultural machinery service centers. In this research, a multiregional agricultural machinery scheduling model with a time window was established considering the timeliness of agricultural machinery operation. This model was divided into two stages: In the first stage, regions were divided through the Voronoi diagram, and farmlands were distributed to intraregional service centers. In the second stage, the model was solved using the hybrid particle swarm optimization (HPSO). The algorithm improves the performance of the algorithm by introducing a crossover, mutation, and particle elimination mechanism, and by using a linear differential to reduce the inertia weight and trigonometric function learning factor. Next, the accuracy and effectiveness of the algorithm are verified by different experimental samples. The results show that the algorithm can effectively reduce the scheduling cost, and has the advantages of strong global optimization ability, high stability, and fast convergence speed. Subsequent algorithm comparison proves that HPSO has better performance in different situations, can effectively solve the scheduling problem, and provides a reasonable scheduling scheme for multiarea and multifarmland operations. |
| first_indexed | 2024-03-11T04:01:17Z |
| format | Article |
| id | doaj.art-21db08477d944bd89cb7ac10161c230c |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T04:01:17Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-21db08477d944bd89cb7ac10161c230c2023-11-18T00:03:07ZengMDPI AGAgriculture2077-04722023-05-01135104210.3390/agriculture13051042A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization AlgorithmHuang Huang0Xinwei Cuan1Zhuo Chen2Lina Zhang3Hao Chen4College of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaChinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaThe reasonable scheduling of agricultural machinery can avoid their purposeless flow during the operational service and reduce the scheduling cost of agricultural machinery service centers. In this research, a multiregional agricultural machinery scheduling model with a time window was established considering the timeliness of agricultural machinery operation. This model was divided into two stages: In the first stage, regions were divided through the Voronoi diagram, and farmlands were distributed to intraregional service centers. In the second stage, the model was solved using the hybrid particle swarm optimization (HPSO). The algorithm improves the performance of the algorithm by introducing a crossover, mutation, and particle elimination mechanism, and by using a linear differential to reduce the inertia weight and trigonometric function learning factor. Next, the accuracy and effectiveness of the algorithm are verified by different experimental samples. The results show that the algorithm can effectively reduce the scheduling cost, and has the advantages of strong global optimization ability, high stability, and fast convergence speed. Subsequent algorithm comparison proves that HPSO has better performance in different situations, can effectively solve the scheduling problem, and provides a reasonable scheduling scheme for multiarea and multifarmland operations.https://www.mdpi.com/2077-0472/13/5/1042scheduling modeltime windowtwo-stage algorithmVoronoi diagramparticle swarm arithmetic |
| spellingShingle | Huang Huang Xinwei Cuan Zhuo Chen Lina Zhang Hao Chen A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm Agriculture scheduling model time window two-stage algorithm Voronoi diagram particle swarm arithmetic |
| title | A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm |
| title_full | A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm |
| title_fullStr | A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm |
| title_full_unstemmed | A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm |
| title_short | A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm |
| title_sort | multiregional agricultural machinery scheduling method based on hybrid particle swarm optimization algorithm |
| topic | scheduling model time window two-stage algorithm Voronoi diagram particle swarm arithmetic |
| url | https://www.mdpi.com/2077-0472/13/5/1042 |
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