| 深松作业下多机协同任务分配优化方法 |
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| 引用本文： | 李丽,侯兴华,陈行政,王五一,周浩.深松作业下多机协同任务分配优化方法[J].农业工程学报,2023,39(21):1-9. |
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| 作者姓名： | 李丽 侯兴华 陈行政 王五一 周浩 |
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| 作者单位： | 西南大学工程技术学院, 重庆 400715;宜宾西南大学研究院, 宜宾 644002 |
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| 基金项目： | 重庆市技术创新与应用发展专项重点项目（cstc2021jscx-gksbX0065）；宜宾市双城协议保障科研项目（XNDX2022020015） |
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| 摘 要： | 任务分配方式对农业机械作业油耗影响显著。在多机协同多任务分配过程中，与将完整田块作业任务分配给单台农业机械相比，拆分各任务田块作业量并采用多农机协同作业能进一步降低作业油耗。为实现农业机群高效节能协同作业，该研究以深松作业为例，首先系统地分析机群协同作业过程油耗；然后建立以农机作业量为优化变量、以最低机群油耗和最短整体耗时为优化目标的多目标优化模型，并采用INSGA-II算法对模型进行优化求解；最后基于实际深松作业案例，分析了所提模型的有效性和实用性。案例分析结果表明：与传统NSGA-II算法相比，INSGA-Ⅱ算法可降低4.35%机群油耗及4.51%整体耗时；与传统作业方式相比，根据本文模型进行多机协同任务分配可降低5.51%机群油耗及42.65%整体耗时。研究结果可为无人农场多任务多农机作业分配提供科学依据。
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| 关 键 词： | 农业机械 油耗 任务分配 多机协同 INSGA-Ⅱ算法 |
| 收稿时间： | 2023/5/16 0:00:00 |
| 修稿时间： | 2023/10/28 0:00:00 |
Optimizing multi-machine task allocation for deep loosening operations |
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| LI Li,HOU Xinghu,CHEN Xingzheng,WANG Wuyi,ZHOU Hao.Optimizing multi-machine task allocation for deep loosening operations[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(21):1-9. |
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| Authors: | LI Li HOU Xinghu CHEN Xingzheng WANG Wuyi ZHOU Hao |
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| Institution: | College of Engineering and Technology, Southwest University, Chongqing 400715, China;Yibin Academy of Southwest University, Southwest University, Yibin 644002, China |
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| Abstract: | Task allocation can dominate the energy consumption in agricultural equipment during operations. Particularly, energy efficiency can promote sustainable development in the context of carbon emissions peaking and carbon neutrality in modern agriculture. Among them, tasks can be programmed into integral parts of operations in multiple agricultural machinery. In the realm of collaborative multitasking, multiple agricultural machines can be utilized to execute the operation tasks within individual fields. The well-defined partitioning of their duties can substantially reduce the overlapping coverage areas. As such, this allocation can then translate into a more effective reduction in energy consumption. Furthermore, partial idle time can be offered for agricultural machinery, when the number of tasks exceeds the available number of machines. The task allocation can also have the potential to enable the simultaneous deployment of multiple agricultural machines within the same field. In this study, a tradeoff was provided for the operational pathway and the number of rows. The operational area was then established for each agricultural machine. The potential conflicts were also effectively resolved in the course of the multi-machine collaboration. Taking the deep tillage operation model as a case study, both effective and energy-efficient collaborative operations were achieved within the agricultural machinery group. To this end, a systematic analysis was performed on the energy consumption during collaborative operations. Subsequently, a multi-objective optimization was proposed to minimize the energy consumption and the completion time of the agricultural machinery group, wherein the task volume was set as the optimization variable. INSGA-II was employed to optimize this model, including the initialization stage, logistic mapping and incorporating, as well as reverse learning. An improved heuristic method of population initialization was employed to enhance the population diversity. In addition, the barrier function was designed to prevent the solution in local optima. Moreover, the uniform crossover operator and simulated single-point mutation were combined to ensure the integrity of integer encoding. The efficacy and practicality of the improved model were systematically evaluated in real-world case studies using deep loosening operations. The results demonstrate that the improved model achieved a notable 4.35% reduction in machinery group energy consumption, and a 4.51% reduction in completion time, compared with the conventional NSGA-II. Furthermore, there was a remarkable 5.51% reduction in the machinery group energy consumption and an impressive 42.65% reduction in the completion time, compared with conventional operations. In essence, the optimization fully met the immediate needs of task allocation across various work scenarios during collaborative multi-machine operations in practical agricultural production. For instance, the allocation of operating rows can be expected to efficiently define the working areas in the seeding and harvesting machines with different working widths, compared with the conventional parallel operation. This innovation can also lead to more optimal solutions in group task allocation. |
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| Keywords: | agricultural machinery fuel consumption task allocation multi-machine cooperative INSGA-II algorithm |
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