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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Veröffentlicht in:	AgriEngineering Jg. 7; H. 6; S. 197
Hauptverfasser:	Jia, Liruizhi, Zhang, Qinshuo, Liu, Shengquan, Kong, Bo, Liu, Yuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2025
Schlagworte:	Agricultural cooperatives Agricultural equipment agricultural machinery operation service Agricultural production Agricultural technology Algorithms Clustering Collaboration Cooperation Design Efficiency Evolutionary algorithms Farm machinery Farmers Genetic algorithms Integer programming Methods multi-objective evolutionary algorithm multi-station agricultural machinery scheduling Multiple objective analysis Optimization algorithms Path planning Planning Regions reinforcement learning Scheduling Search methods
ISSN:	2624-7402, 2624-7402
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Abstract	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.
AbstractList	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.
Author	Liu, Yuan Zhang, Qinshuo Jia, Liruizhi Liu, Shengquan Kong, Bo
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SubjectTerms	Agricultural cooperatives Agricultural equipment agricultural machinery operation service Agricultural production Agricultural technology Algorithms Clustering Collaboration Cooperation Design Efficiency Evolutionary algorithms Farm machinery Farmers Genetic algorithms Integer programming Methods multi-objective evolutionary algorithm multi-station agricultural machinery scheduling Multiple objective analysis Optimization algorithms Path planning Planning Regions reinforcement learning Scheduling Search methods
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Title	Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm
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