| 基于模糊隶属度的多站点多机协同即时响应调度系统 |
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| 引用本文： | 黄凰,陈燕燕,朱明,刘亚东,张镇府.基于模糊隶属度的多站点多机协同即时响应调度系统[J].农业工程学报,2021,37(21):71-79. |
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| 作者姓名： | 黄凰 陈燕燕 朱明 刘亚东 张镇府 |
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| 作者单位： | 1. 华中农业大学工学院，武汉 430070； 2. 农业农村部长江中下游农业装备重点实验室，武汉 430070 |
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| 基金项目： | 国家自然科学基金项目（71503095）；湖北省农业科技创新行动；中央高校基本科研业务费专项资金资助项目（2662015QC017，2662014BQ037）；中国工程院咨询项目（2019-ZD-5） |
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| 摘 要： | 为了实现多农机站联合调配完成农户的实时作业订单，该研究针对农田与农机的匹配与调度需求问题，综合考虑农户满意度、多农机站协同、订单数量、农田面积和位置坐标等因素，建立带有模糊时间窗并以调度总时长最小和调度农机数量最少为目标的多农机站即时响应调度数学模型。并设计了基于保留优秀父代基因的改进遗传算法的农机调度系统，完成多农机站响应多农田的同时作业需求的任务，在最短时间里即时调配农机按照最短路径至各农田完成作业要求。以武汉周边某地区的3个农机站和35个农田作业订单为例，验证所提出的模型和智能优化算法，并进行可视化界面展示。试验表明，当模糊隶属度为0.8时，调度总路程减少率为9.89%，农机数量降低率为15.38%；针对该地区各农机站农机数量的实际情况，在不影响农户满意度的前提下，单个农机站接受实时订单数量以不超过20为最佳。该研究实现了多农机站对多农田精准调度作业，有助于科学合理调度农机，提高农机作业效率，节约成本投入。
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| 关 键 词： | 农业机械 调度 模糊时间窗 遗传算法 即时响应 |
| 收稿时间： | 2021/6/4 0:00:00 |
| 修稿时间： | 2021/10/23 0:00:00 |
Multi-site and multi-machine cooperative instant response scheduling system based on fuzzy membership |
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| Huang Huang,Chen Yanyan,Zhu Ming,Liu Yadong,Zhang Zhenfu.Multi-site and multi-machine cooperative instant response scheduling system based on fuzzy membership[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(21):71-79. |
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| Authors: | Huang Huang Chen Yanyan Zhu Ming Liu Yadong Zhang Zhenfu |
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| Institution: | 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China;2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, 430070, China |
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| Abstract: | Abstract: This study aims to realize the joint deployment of multiple agricultural machinery stations, particularly for the real-time job orders. A mathematical model with a fuzzy time window was also established to minimize the total scheduling time and the number of dispatching agricultural machinery. Some factors were comprehensively considered, such as farmers'' satisfaction, the cooperation of multiple agricultural machinery stations, the number of orders, the area of farmland, and the location coordinates. An improved genetic method (GA) with excellent parent genes was designed to fulfill the task of multi machine station responding to the demand of multi farmland. At the same time, the agricultural machinery was allocated in the shortest time to implement the operation requirements of each farmland, according to the shortest path. A case study was carried out to verify the model and the visual interface, including three stations of agricultural machinery and 35 operation orders of farmland in a certain area around Wuhan, Hubei Province of China. The results showed that an excellent searching and stable convergence were achieved in the scheduling system of agricultural machinery. Specifically, the reduction rate of the total scheduling distance was 9.89%, and the reduction rate of the number of agricultural machinery was 15.38%, when the fuzzy membership degree was 0.8. An optimal number of real-time orders accepted by a single farm station was not more than 20, according to the actual situation of the agricultural machinery quantity in each station. Furthermore, the improved GA presented a better performance than the hybrid genetic in general, indicating the less calculation time of the deployment, the more reasonable allocation of tasks, and the reduced scheduling distance. The multi-site and multi-machine cooperative instant repose scheduling was also considered the joint deployment agricultural machinery and fuzzy time window in the modeling. There was a higher accuracy of the scheduling operation on agricultural machinery, and the fully considered satisfaction of farmers, even though the complexity of model increased, compared with the scheduling operation at a single agricultural machinery station. In the scheduling algorithm, the crossover and mutation operators were improved to reduce the risk of the operation data falling into the local optimal solution with the less running time. Consequently, the scheme can be widely expected to completely deal with agricultural machinery scheduling under complex backgrounds, fully meeting the cooperative operation of multiple agricultural machinery stations for the real-time operation needs of farmers. This finding can provide a strong support to the cost-saving and high efficiency of operation on agricultural machinery in modern agriculture. |
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| Keywords: | agricultural machinery dispatching fuzzy time window genetic algorithm immediate response |
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