Research on the scheduling model of flying defense team based on simulated annealing algorithm

doi:10.12398/j.issn.2096-7217.2022.02.001

Abstract

Abstract: Plant protection UAVs (unmanned aerial vehicles) are usually operated in the form of flying defense teams. At present, the scheduling organization of the flying defense teams is relatively extensive, resulting in the low efficiency of the flying defense teams. Reasonable scheduling of UAVs can improve the operational efficiency of the flying defense teams. By taking the rice smut prevention and control scenarios as the research object, according to the operation specifications of “single-spray triple-prevention” against rice smut, referring to the current situation of the order modes of flying defense operation, an order management and plant protection UAVs scheduling model is proposed in this study, to ensure the operation quality and operation efficiency of plant protection UAVs. The model has two parts: (1) Order management, which is an order sorting method that comprehensively considers order work area, time window, and order urgency; (2) Scheduling model, which is a UAV scheduling model based on simulated annealing algorithm. Taking 16 plots in Nanjing area in China and rice smut control tasks of 4 flying defense teams for case study, the simulated annealing algorithm and the greedy algorithm were used to make a comparative study on the time window lengths of 3-6 days. Research results showed that, when the operation time window length is 3-5 days, the longer the time window, the shorter the scheduling distance and waiting time, the longer the total operation time, and the higher the total revenue. When the time window length is 6 days, the total operation time and operation income will not change. This research can provide a scientific basis for the deployment and decision-making analysis of the UAVs flying defense teams, and provide a reference for the development of the intelligent scheduling system for agricultural machinery.

Key words: plant protection unmanned aerial vehicles, scheduling model, task assignment, simulated annealing algorithm

CLC Number:

Li Yibai, Cao Gunagqiao. Research on the scheduling model of flying defense team based on simulated annealing algorithm[J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2022, 3(2): 1-9.

References

［1］Huang S W, Liu L M, Wang L, et al. Research on advance of (Cooke) Takah worldwide: Part I. Research status of rice false smut ［J］. Journal of Agricultural Science, 2019, 11(15): 240-250.
［2］Naila S, Yu J J, Yang N, et al. A rapid recognition method for rice false smut based on HOG features and SVM classification ［C］//Journal of Physics: Conference Series, 2020, 1576(1): 012018.
［3］Wu N, Jiang H B, Bao Y D, et al. Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions ［J］. Sensors and Actuators B: Chemical, 2020, 308: 127696.
［4］Karuppiah K, Senthilkumar T, Jayas D S, et al. Detection of fungal infection in five different pulses using near-infrared hyperspectral imaging ［J］. Journal of Stored Products Research, 2016, 65: 13-18.
［5］Mogili U R, Deepak B. Review on application of drone systems in precision agriculture ［J］. Procedia Computer Science, 2018, 133: 502-509.
［6］Xu B, Chen L P, Xu M, et al. Path planning algorithm for plant protection UAVs in multiple operation areas ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(2): 75-81. (in Chinese)
［7］Torres M, Pelta D A, Verdegay J L, et al. Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction ［J］. Expert Systems with Applications, 2016, 55: 441-451.
［8］Amorim J C, Alves V, Freitas E. Assessing a swarmgap based solution for the task allocation problem in dynamic scenarios ［J］. Expert Systems with Applications, 2020, 152(19): 113437.
［9］Sun Y, Ren Y, Cai X. Biobjective emergency logistics scheduling model based on uncertain traffic conditions ［J］. Mathematical Problems in Engineering, 2020(5): 1-15.
［10］Ji M, Fang J, Zhang W, et al. Logistics scheduling to minimize the sum of total weighted inventory cost and transport cost ［J］. Computers & Industrial Engineering, 2018, 120: 206-215.
［11］Ding Z, Zhao Z, Liu D, et al. Multi-objective scheduling of relief logistics based on swarm intelligence algorithms and spatio-temporal traffic flow ［J］. Journal of Safety Science and Resilience, 2021, 2(4): 222-229.
［12］Wang Y J, Huang G Q. A two-step framework for dispatching shared agricultural machinery with time windows ［J］. Computers and Electronics in Agriculture, 2022, 192: 106607.
［13］Schmidt J R, Cheein F A. Assessment of power consumption of electric machinery in agricultural tasks for enhancing the route planning problem ［J］. Computers and Electronics in Agriculture, 2019, 163: 104868.
［14］Fang M, Feng X. Research on path planning of plant protection UAV based on grid method and improved ant colony algorithm ［J］. IOP Conference Series Materials Science and Engineering, 2019, 612: 052053.
［15］Yang Z, Zheng L H, Li M Z, et al. Matching algorithm for plant protecting unmanned aerial vehicles and plant protecting jobs based on R-tree spatial index ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(1): 92-98.（in Chinese）
［16］Yang H T, Xiong S M, Frimpong S A, et al. A consortium blockchain-based agricultural machinery scheduling system ［J］. Sensors, 2020, 20(9): 2643.
［17］Wang Z, Yang J, Liu P, et al. Development of an agricultural vehicle levelling system based on rapid active levelling ［J］. Biosystems Engineering, 2019, 186: 337-348.
［18］Carlos G, Borja V, Javier E. An application of the vehicle routing problem to biomass transportation ［J］. Biosystems Engineering, 2014, 124(4): 40-52.
［19］Zhai Z Y, Martínez Ortega J F, Lucas Martínez N, et al. A mission planning approach for precision farming systems based on multi-objective optimization ［J］. Sensors, 2018, 18(6): 1795.
［20］Ban H B, Nguyen P K. A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem ［J］. Computational Social Networks, 2021, 8: 3.
［21］Just G E, Pellenz M E, Lima L, et al. UAV Path optimization for precision agriculture wireless sensor networks ［J］. Sensors, 2020, 20(21): 6098.
［22］Tian R, Cao M Y, Ma F Y, et al. Agricultural UAV path planning based on improved A* and gravity search mixed algorithm ［J］. Journal of Physics: Conference Series, 2020, 1631: 12082.
［23］Redi A, Jewpanya P, Kurniawan A C, et al. A simulated annealing algorithm for solving two-echelon vehicle routing problem with locker facilities ［J］. Algorithms, 2020, 13(9): 218.
［24］Normasari N, Yu V F, Bachtiyar C, et al. A simulated annealing heuristic for the capacitated green vehicle routing problem ［J］. Mathematical Problems in Engineering, 2019(1): 1-18.

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