Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm
The multi-station agricultural machinery scheduling process mainly involves two key stages: order allocation and path planning. Order allocation methods based solely on spatial distance cannot ensure the continuity of agricultural operations. Multi-objective evolutionary algorithms are sensitive to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/6/197 |
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| author | Liruizhi Jia Qinshuo Zhang Shengquan Liu Bo Kong Yuan Liu |
| author_facet | Liruizhi Jia Qinshuo Zhang Shengquan Liu Bo Kong Yuan Liu |
| author_sort | Liruizhi Jia |
| collection | DOAJ |
| description | The multi-station agricultural machinery scheduling process mainly involves two key stages: order allocation and path planning. Order allocation methods based solely on spatial distance cannot ensure the continuity of agricultural operations. Multi-objective evolutionary algorithms are sensitive to the initial population quality and local search strategies for path planning, where unreasonable initial solutions or improper local search strategies can affect the diversity of solutions. Therefore, we propose a spatiotemporal allocation algorithm that constructs a spatiotemporal distance function to describe the feasibility of continuous operations and evaluates the spatiotemporal proximity of operation points and stations for clustering allocation. In terms of path planning, we design a learnable multi-objective evolutionary algorithm (LMOEA). First, a hybrid initialization strategy is used to enhance the initial population quality; second, a Q-learning-based local search method is constructed to adaptively adjust the search strategy to reduce ineffective iterations; finally, a dynamically adjusted crowding distance mechanism is introduced to improve the distribution of the solution set. Experimental results show that the spatiotemporal allocation algorithm improves the average cost and satisfaction by 4.09% and 3.28% compared to the spatial method. Compared with INSGA-II, HTSMOGA, and NNITSA algorithms, the LMOEA can obtain solutions of higher quality and greater diversity. |
| format | Article |
| id | doaj-art-b86ccb1a25e34bcca6f6ba72ece5636b |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-b86ccb1a25e34bcca6f6ba72ece5636b2025-08-20T03:26:14ZengMDPI AGAgriEngineering2624-74022025-06-017619710.3390/agriengineering7060197Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary AlgorithmLiruizhi Jia0Qinshuo Zhang1Shengquan Liu2Bo Kong3Yuan Liu4College of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaThe multi-station agricultural machinery scheduling process mainly involves two key stages: order allocation and path planning. Order allocation methods based solely on spatial distance cannot ensure the continuity of agricultural operations. Multi-objective evolutionary algorithms are sensitive to the initial population quality and local search strategies for path planning, where unreasonable initial solutions or improper local search strategies can affect the diversity of solutions. Therefore, we propose a spatiotemporal allocation algorithm that constructs a spatiotemporal distance function to describe the feasibility of continuous operations and evaluates the spatiotemporal proximity of operation points and stations for clustering allocation. In terms of path planning, we design a learnable multi-objective evolutionary algorithm (LMOEA). First, a hybrid initialization strategy is used to enhance the initial population quality; second, a Q-learning-based local search method is constructed to adaptively adjust the search strategy to reduce ineffective iterations; finally, a dynamically adjusted crowding distance mechanism is introduced to improve the distribution of the solution set. Experimental results show that the spatiotemporal allocation algorithm improves the average cost and satisfaction by 4.09% and 3.28% compared to the spatial method. Compared with INSGA-II, HTSMOGA, and NNITSA algorithms, the LMOEA can obtain solutions of higher quality and greater diversity.https://www.mdpi.com/2624-7402/7/6/197agricultural machinery operation servicemulti-station agricultural machinery schedulingmulti-objective evolutionary algorithmreinforcement learning |
| spellingShingle | Liruizhi Jia Qinshuo Zhang Shengquan Liu Bo Kong Yuan Liu Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm AgriEngineering agricultural machinery operation service multi-station agricultural machinery scheduling multi-objective evolutionary algorithm reinforcement learning |
| title | Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm |
| title_full | Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm |
| title_fullStr | Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm |
| title_full_unstemmed | Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm |
| title_short | Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm |
| title_sort | multi station agricultural machinery scheduling based on spatiotemporal clustering and learnable multi objective evolutionary algorithm |
| topic | agricultural machinery operation service multi-station agricultural machinery scheduling multi-objective evolutionary algorithm reinforcement learning |
| url | https://www.mdpi.com/2624-7402/7/6/197 |
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