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A collaborative scheduling and planning method for multiple machines in harvesting and transportation operations-part II: Scheduling and planning of harvesters and grain trucks

文献类型： 外文期刊

作者： Wang, Ning 1 ; Li, Shunda 1 ; Xiao, Jianxing 2 ; Wang, Tianhai 2 ; Han, Yuxiao 1 ; Wang, Hao 3 ; Zhang, Man 1 ; Li, Han 1 ;

作者机构： 1.China Agr Univ, Key Lab Smart Agr Syst Integrat, Minist Educ, Beijing 100083, Peoples R China

2.China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr & Rural Affairs, Beijing 100083, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

4.State Key Lab Intelligent Agr Power Equipment, Beijing 100097, Peoples R China

关键词： Multiple machines; Collaborative scheduling; Harvester unloading points; Scheduling model; 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 235 卷

页码：

收录情况： SCI

摘要： In Part I of this two-part paper (A Collaborative Scheduling and Planning Method for Multiple Machines in Harvesting and Transportation Operations-Part I: Harvester Task Allocation and Sequence Optimization), the primary focus was to address the issue of collaborative scheduling for harvesters through task allocation and whole-process path planning. In this paper (Part II), the emphasis shifts to addressing the collaborative scheduling and planning of both harvesters and grain trucks while considering the efficiency of grain trucks. First, a novel algorithm named the Headland area Unloading-based Harvester Unloading Point Generation and Adjustment algorithm (HU-HUPGA) was proposed, which can generate and adjust the position of unloading points based on the harvester's operational path. This method can effectively reduce the complexity of grain truck paths while preventing the trucks from entering the plot and crushing the crops. Next, a scheduling and planning model for multiple grain trucks was constructed, and a hybrid genetic and heuristic iterative (HGHI) algorithm was proposed to solve the model. The method fully utilizes the genetic algorithm's global search capability and the heuristic method's local optimization capability. It not only improves the quality and accuracy of the solution but also speeds up the optimization process. Finally, using the generated sequence of harvester unloading locations, the operation schedules and paths of both grain trucks and harvesters were updated. The experimental results demonstrate that the HU-HUPGA method has effectively generated and adjusted harvester unloading points within the field, ensuring their precise location in the designated headland area. The HGHI algorithm effectively addresses the collaborative scheduling problem for grain trucks while simultaneously implementing their path planning through a dedicated path planning method. This study, comprising Part I and Part II, provides theoretical and technical support for the collaborative scheduling and planning of different types of agricultural machines.

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