Optimising the maintenance strategy for a multi-AGV system using genetic algorithms

被引:0
|
作者
Yan, R. D. [1 ]
Dunnett, S. J. [1 ]
Jackson, L. M. [1 ]
机构
[1] Loughborough Univ, Loughborough, Leics, England
基金
英国工程与自然科学研究理事会;
关键词
COLORED PETRI-NET; OPTIMIZATION; DESIGN; GA;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Automated Guided Vehicles (AGVs) are playing increasingly vital roles in a variety of applications in modern society, such as intelligent transportation in warehouses and material distribution in automated production lines. They improve production efficiency, save labour cost, and bring significant economic benefit to end users. However, to utilise these potential benefits is highly dependent on the reliability and availability of the AGVs. In other words, an effective maintenance strategy is critical in the application of AGVs. The research activity reported in this paper is to realise an effective maintenance strategy for a multi-AGV system by the approach of Genetic Algorithms (GA). To facilitate the research, an automated material distribution system consisting of three AGVs is considered in this paper for methodology development. The movement of every AGV in the multi-AGV system, and the corrective and periodic preventive maintenances of failed AGVs are modelled using the approach of Coloured Petri Nets (CPNs). Then, a GA is adopted for optimising the maintenance and associated design and operation of the multi-AGV system. From this research, it is disclosed that both the location selection of the maintenance site and the maintenance strategies that are adopted for AGV maintenance have significant influences on the efficiency, cost, and productivity of a multi-AGV system.
引用
收藏
页码:547 / 554
页数:8
相关论文
共 50 条
  • [1] Novel methodology for optimising the design, operation and maintenance of a multi-AGV system
    Yan, Rundong
    Dunnett, S. J.
    Jackson, L. M.
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2018, 178 : 130 - 139
  • [2] Design of multi-AGV intelligent collision avoidance system based on dynamic priority strategy
    Liu, Tiegang
    Liang, Zhuguan
    [J]. 2022 ASIA CONFERENCE ON ADVANCED ROBOTICS, AUTOMATION, AND CONTROL ENGINEERING (ARACE 2022), 2022, : 47 - 51
  • [3] Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop
    Zhao, Nan
    Feng, Chun
    [J]. MATHEMATICS, 2023, 11 (16)
  • [4] Mission Scheduling of Multi-AGV System with Dynamic Simulation
    Bao, Bizhen
    Duan, Zhao
    Chen, Wei
    [J]. 2020 INTERNATIONAL SYMPOSIUM ON AUTONOMOUS SYSTEMS (ISAS), 2020, : 115 - 120
  • [5] A Dynamic Task Assignment Optimization Method for Multi-AGV System Based on Genetic Algorithm
    Song, Shuan-Jun
    Peng, Long-Guang
    Zhang, Jie
    Liu, Zhen
    [J]. Journal of Computers (Taiwan), 2023, 34 (03) : 93 - 108
  • [6] A Novel Multi-AGV Coordination Strategy Based on the Combination of Nodes and Grids
    Chen, Xinyu
    Wu, Weimin
    Hu, Ruifen
    [J]. IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (03) : 6218 - 6225
  • [7] A System Control Strategy of a Conflict-free Multi-AGV Routing based on Improved A* Algorithm
    Jia, Fang
    Ren, Chenglong
    Chen, Yi
    Xu, Zhixiang
    [J]. 2017 24TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND MACHINE VISION IN PRACTICE (M2VIP), 2017, : 199 - 204
  • [8] The Scheduling Technology Development of Multi-AGV System in AI Era
    Huo, Dengya
    Wu, Yaohua
    Wu, Shasha
    [J]. 5TH ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI2020), 2020, 1575
  • [9] Big data encrypting transmission framework for a multi-AGV system
    Zhang, Tongpo
    Wan, Yuxin
    Lopez-Benitez, Miguel
    Lim, Enggee
    Ma, Fei
    Yu, Limin
    [J]. 2022 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY, CYBERC, 2022, : 50 - 56
  • [10] Multi-AGV path planning with double-path constraints by using an improved genetic algorithm
    Han, Zengliang
    Wang, Dongqing
    Liu, Feng
    Zhao, Zhiyong
    [J]. PLOS ONE, 2017, 12 (07):