A collaborative scheduling and planning method for multiple machines in harvesting and transportation operations-Part I: Harvester task allocation and sequence optimization

文献类型： 外文期刊

第一作者： Wang, Ning

作者： Wang, Ning;Li, Shunda;Han, Yuxiao;Zhang, Man;Li, Han;Wang, Ning;Xiao, Jianxing;Wang, Tianhai;Zhang, Man;Wang, Hao;Wang, Hao

作者机构：

关键词： Agricultural machine; Collaborative scheduling; Scheduling model; Electronic map; Path planning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 232 卷

页码：

收录情况： SCI

摘要： In the scenario of harvesting-transportation operation, the collaborative scheduling of harvesters and grain trucks is crucial for addressing the challenge of scheduling different types of agricultural machinery in farm areas. During the harvest, the harvesters and grain trucks must cooperate within a short time window. This study is divided into two parts (Part I and Part II), focusing on the collaborative scheduling problem of the harvesters, and operation coordination between harvesters and grain trucks, respectively. In this paper (Part I), we focus on addressing the problem of harvester task allocation and path planning. First, the topological map method was used to define the topological structure and construct an electronic map of the farm. Then, a multi-harvester task allocation model was built, and a greedy minimum-maximum load balancing algorithm based on the nearestneighbor heuristic (GMM-LB-NNH) algorithm was proposed to solve the model and obtain the task sequence for the harvesters. Finally, based on the task sequence, the whole-process path planning for the harvester was completed. We conducted simulation tests of harvester task allocation and whole-process path planning experiments for harvesters using the electronic map we developed. The results demonstrate that the proposed method effectively achieves harvester task allocation and path planning. Additionally, it significantly reduces overall operation time by an average of 29.8 min compared to the Ant Colony Optimization algorithm and by 12.6 min compared to the Genetic Algorithm, providing a novel approach for the scheduling and planning of the same types of agricultural machinery.

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