Scheduling Model of UAV Plant Protection Team Based on Multi-objective Optimization
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    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.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:August 01,2019
  • Revised:
  • Adopted:
  • Online: November 10,2019
  • Published:
Article QR Code