Operations planning for agricultural harvesters using ant colony optimization

被引:24
|
作者
Bakhtiari, A. [1 ]
Navid, H. [1 ]
Mehri, J. [2 ]
Berruto, R. [3 ]
Bochtis, D. D. [4 ]
机构
[1] Univ Tabriz, Fac Agr, Dept Agr Machinery Engn, Tabriz, Iran
[2] Univ Tabriz, Fac Math, Dept Appl Math, Tabriz, Iran
[3] Univ Turin, Fac Agr, DEIAFA, Turin, Italy
[4] Aarhus Univ, Fac Sci & Technol, Dept Engn, DK-8000 Aarhus C, Denmark
关键词
B-patterns; harvesting planning; field efficiency;
D O I
10.5424/sjar/2013113-3865
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
An approach based on ant colony optimization for the generation for optimal field coverage plans for the harvesting operations using the optimal track sequence principle B-patterns was presented. The case where the harvester unloads to a stationary facility located out of the field area, or in the field boundary, was examined. In this operation type there are capacity constraints to the load that a primary unit, or a harvester in this specific case, can carry and consequently, it is not able to complete the task of harvesting a field area and therefore it has to leave the field area, to unload, and return to continue the task one or more times. Results from comparing the optimal plans with conventional plans generated by operators show reductions in the in-field nonworking distance in the range of 19.3-42.1% while the savings in the total non-working distance were in the range of 18-43.8%. These savings provide a high potential for the implementation of the ant colony optimization approach for the case of harvesting operations that are not supported by transport carts for the out-of-the-field removal of the crops, a practice case that is normally followed in developing countries, due to lack of resources.
引用
收藏
页码:652 / 660
页数:9
相关论文
共 50 条
  • [41] Ant colony optimization for assembly sequence planning based on parameters optimization
    Zunpu HAN
    Yong WANG
    De TIAN
    Frontiers of Mechanical Engineering, 2021, (02) : 393 - 409
  • [42] Ant colony optimization for assembly sequence planning based on parameters optimization
    Han, Zunpu
    Wang, Yong
    Tian, De
    FRONTIERS OF MECHANICAL ENGINEERING, 2021, 16 (02) : 393 - 409
  • [43] Topology Optimization of Structures Using Ant Colony Optimization
    Wu, Chun-Yin
    Zhang, Ching-Bin
    Wang, Chi-Jer
    WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09), 2009, : 601 - 607
  • [44] Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm
    Uriol, Ronald
    Moran, Antonio
    2017 3RD INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR), 2017, : 15 - 21
  • [45] Disassembly sequence planning using component-joint graph and ant colony optimization
    Zheng Menglei
    Tian Ling
    Liu Beibei
    ComputerAidedDrafting,DesignandManufacturing, 2016, (01) : 54 - 57
  • [46] Intelligent planning of fire evacuation routes using an improved ant colony optimization algorithm
    Xu, Lei
    Huang, Kai
    Liu, Jiepeng
    Li, Dongsheng
    Chen, Y. Frank
    JOURNAL OF BUILDING ENGINEERING, 2022, 61
  • [47] Robot path planning using fusion algorithm of ant colony optimization and genetic algorithm
    Ma, Kangkang
    Wang, Lei
    Cai, Jingcao
    Li, Dongdong
    Wang, Anheng
    Tan, Tielong
    INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING, 2023, 14 (06)
  • [48] Bilevel model for production-distribution planning solved by using ant colony optimization
    Calvete, Herminia I.
    Gale, Carmen
    Oliveros, Maria-Jose
    COMPUTERS & OPERATIONS RESEARCH, 2011, 38 (01) : 320 - 327
  • [49] FPGA-Based Path Planning Using Improved Ant Colony Optimization Algorithm
    Hsu, Chen-Chien
    Hou, Ru-Yu
    Kao, Wen-Chung
    Li, Shih-An
    2015 IEEE 5TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - BERLIN (ICCE-BERLIN), 2015, : 443 - 444
  • [50] Path planning for unmanned vehicles using ant colony optimization on a dynamic Voronoi diagram
    Li, YH
    Dong, T
    Bikdash, M
    Song, YD
    ICAI '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, 2005, : 716 - 721