曹光乔,张庆凯,陈聪,张萌,张进龙,黄玉祥.基于多目标优化的飞防队作业调度模型研究[J].农业机械学报,2019,50(11):92-101.
CAO Guangqiao,ZHANG Qingkai,CHEN Cong,ZHANG Meng,ZHANG Jinlong,HUANG Yuxiang.Scheduling Model of UAV Plant Protection Team Based on Multi-objective Optimization[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(11):92-101.
摘要点击次数: 1652
全文下载次数: 780
基于多目标优化的飞防队作业调度模型研究   [下载全文]
Scheduling Model of UAV Plant Protection Team Based on Multi-objective Optimization   [Download Pdf][in English]
投稿时间:2019-08-01  
DOI:10.6041/j.issn.1000-1298.2019.11.010
中文关键词:  植保飞防队  调度模型  多目标优化  时间窗
基金项目:国家重点研发计划项目(2017YFD0700601-2)、教育部人文社科基金项目(14YJCZH060)和中国农业科学院科技创新工程项目(农科院办(2014)216号)
作者单位
曹光乔 农业农村部南京农业机械化研究所 
张庆凯 农业农村部南京农业机械化研究所
西北农林科技大学 
陈聪 农业农村部南京农业机械化研究所 
张萌 农业农村部南京农业机械化研究所 
张进龙 农业农村部南京农业机械化研究所 
黄玉祥 西北农林科技大学 
中文摘要:针对面向植保服务订单的多飞防队协同作业模式,综合考虑订单时间窗、病虫害侵染状况、多机协同等关键因素,建立以作业总收益最大、作业总时长最小为优化目标的飞防队作业调度模型;设计了订单优先级排序算法和基于带精英策略的非支配排序遗传算法(NSGA-Ⅱ)的作业路径规划算法,并对调度模型进行了求解。以陕西省武功县植保作业为例,对飞防队作业调度模型及算法进行了验证,实验表明,建立的模型及算法能输出满足时间窗约束的Pareto最优解集,具有良好的搜索性能以及稳定的收敛性能。该研究可为无人机飞防队的调配与决策分析提供科学依据,为农机智能调度系统开发提供参考。
CAO Guangqiao  ZHANG Qingkai  CHEN Cong  ZHANG Meng  ZHANG Jinlong  HUANG Yuxiang
Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture and Rural Affairs,Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture and Rural Affairs;Northwest A&F University,Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture and Rural Affairs,Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture and Rural Affairs,Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture and Rural Affairs and Northwest A&F University
Key Words:UAV plant protection team  scheduling model  multi-objective optimization  time window
Abstract:In recent years, with the development of technologies of agricultural aviation plant protection, plant protection drones was used more and more in the prevention and control of pests and diseases. Aiming at the multi-team collaborative operation mode for plant protection orders in the region, the key factors such as order time window, farmland infection status and multi-machine coordination were comprehensively considered, and a multi-objective scheduling model for UAV plant protection team with the maximum total revenue and the shortest total operation time as the optimization objective was established. The order priority sorting algorithm and the path planning algorithm based on the non-dominated sorting genetic algorithm (NSGA-Ⅱ) were designed to solve the model. A case study of plant protection operations in Wugong County, Shaanxi Province was carried out to illustrate the validity of the proposed model and algorithm. Experiments showed that the model and algorithm designed can output the Pareto optimal solution set satisfying the time window constraint, and can give reasonable transfer path and time arrangement for UAV plant protection team. Moreover, the algorithm had good search performance and stable convergence performance, which can meet the needs of real scheduling problems. At the same time, research showed that increasing the order window time was beneficial to increase the total operating income and reduce the total operation time. The research can provide a scientific basis for the deployment and decision analysis of the UAV plant protection team, and provide reference for the development of the agricultural machinery intelligent dispatching system.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

   下载PDF阅读器