Research article Special Issues

Research on multi-load AGV path planning of weaving workshop based on time priority

  • Received: 24 November 2018 Accepted: 25 February 2019 Published: 15 March 2019
  • The multi-load AGV (Automatic Guided Vehicle) is a new kind of materials handling equipment used to load cloth automatically in an intelligent weaving workshop. It can transport multiple rolls of cloth and choose the correct, most effective path to improve the transportation efficiency without people engaged in. This paper creates a feasible path topology according to the layout of the workshop and the logistics environment, and uses the Warshall-Floyd algorithm to search for the optimal route between two arbitrary points. The aim of the path planning is to maximize the machine efficiency, which is constrained by environmental limits, load limits and work limits. This paper establishes the mathematical model of the path planning problem using the mixed genetic particle swarm optimization algorithm (GA-PSO) to solve the problem, and the particle iteration mechanism based on the time priority is proposed to make the evolution more directional and accelerate the convergence speed of the algorithm. The effectiveness and practicability of the model and methods are verified by simulation and benefit analysis.

    Citation: Li-zhen Du, Shanfu Ke, Zhen Wang, Jing Tao, Lianqing Yu, Hongjun Li. Research on multi-load AGV path planning of weaving workshop based on time priority[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2277-2292. doi: 10.3934/mbe.2019113

    Related Papers:

  • The multi-load AGV (Automatic Guided Vehicle) is a new kind of materials handling equipment used to load cloth automatically in an intelligent weaving workshop. It can transport multiple rolls of cloth and choose the correct, most effective path to improve the transportation efficiency without people engaged in. This paper creates a feasible path topology according to the layout of the workshop and the logistics environment, and uses the Warshall-Floyd algorithm to search for the optimal route between two arbitrary points. The aim of the path planning is to maximize the machine efficiency, which is constrained by environmental limits, load limits and work limits. This paper establishes the mathematical model of the path planning problem using the mixed genetic particle swarm optimization algorithm (GA-PSO) to solve the problem, and the particle iteration mechanism based on the time priority is proposed to make the evolution more directional and accelerate the convergence speed of the algorithm. The effectiveness and practicability of the model and methods are verified by simulation and benefit analysis.


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    [1] H. Fazlollahtabar and M. Saidi-Mehrabad, Models for AGVs' scheduling and routing, Autonomous Guided Vehicles, 20 (2015), 1–15.
    [2] G. M. Li, X. Y. Li, L. Gao, et al., Tasks assigning and sequencing of multiple AGVs based on an improved harmony search algorithm, J. Amb. Inter. Hum. Comp., 2018. Available from: DOI, 10.1007/s12652-018-1137-0.
    [3] D. H. Lee, Resource-based task allocation for multi-robot systems, Robot Auton. Syst., 103 (2018), 151–161.
    [4] T. J. Chen, Y. Sun, W. Dai, et al., On the shortest and conflict-free path planning of multi-AGV system based on dijkstra algorithm and the dynamic time-window method, Adv. Mater. Res., 645 (2013), 267–271.
    [5] A. Kaplan, N. Kingry, P. Uhing, et al., Time-optimal path planning with power schedules for a solar-powered ground robot, IEEE T. Autom. Sci. Eng., 14 (2017), 1235–1244.
    [6] N. Smolic-Rocak, S. Bogdan, Z. Kovacic, et al., Time windows based dynamic routing in multi-AGV systems, IEEE T. Autom. Sci. Eng., 7 (2010), 151–155.
    [7] K. G. Huo, Y. Q. Zhang, Z. H. Hu, et al., Research on scheduling problem of multi-load AGV at automated container terminal, J. Dalian Univ. Techno., 6 (2016), 24–251.
    [8] G. M. Li, B. Zeng, W. Liao, et al., A new AGV scheduling algorithm based on harmony search for material transfer in a real-world manufacturing system, Adv. Mech. Eng., 10 (2018), 1–13.
    [9] M. Mousavi, H. J. Yap, S. N. Musa, et al., Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization, PLoS One, 12 (2017), 1–24.
    [10] F. Hamed and M. Nezam, An optimal path in a bi-criteria AGV-based flexible job shop manufacturing system having uncertain parameters, Int. J. Ind. Syst. Eng., 13 (2010), 27–55.
    [11] H. Fazlollahtabara and M. Saidi-Mehrabad, Optima path in an intelligent AGV based manufacturing system, Transp. Lett., 7 (2015), 219–228.
    [12] V. K. Chawla, A. K. Chanda, S. Angra, et al., Multi-load AGVs scheduling by application of modified memetic particle swarm optimization algorithm, J. Braz. Socy. Mech. Sci., 2018. Available from: 10.1007/s40430-018-1357-4.
    [13] S. Bakshi, Z. Y. Yan, D. M. Chen, et al., A fast algorithm on minimum-time scheduling of an autonomous ground vehicle using a traveling salesman framework, J. Dyn. Syst. Meas. Control, 2018. Available from: DOI, 10.1115/1.4040665.
    [14] H. Fazlollahtabar and S. Hassanli, Hybrid cost and time path planning for multiple autonomous guided vehicles, Appl. Intell., 48 (2018), 482–498.
    [15] M. Grunow, H. Gunther, M. Lehmann, et al., Dispatching multi-load AGVs in highly automatedseaport container terminals, OR Spectrum., 26 (2014), 211–235.
    [16] T. J. Park, J. W. Ahn, C. S. Han, et al., A path generation algorithm of an automatic guided vehicle using sensor scanning method, J. Mech. Sci. Technol., 16 (2002), 137–146.
    [17] H. Y. Wang, Q. Huang, C. T. Li, et al., Graph theory algorithm and MATLAB simulation, BeiJing (2009), Beihang University Press.
    [18] A. Ali and M Tawhid, A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems, Ain. Shams. Eng. J., 8 (2017), 191–206.
    [19] X. Y. Li and L. Gao, An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem, Int. J. Prod. Econ., 174 (2016), 93–110.
    [20] P. B. C. Miranda and R. B. C. Prudêncio, Generation of particle swarm optimization algorithms: An experimental study using grammar guided genetic, Appl. Soft. Comput., 60 (2017), 281–296.
    [21] S. Hamed and K. Govindan, A hybrid particle swarm optimization and genetic algorithm for closed loop supply chain network design in large scale networks, Appl. Math. Model., 39 (2015), 3990–4012.
    [22] K. Deb and N. Padhye, Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms, Comput. Optim. Appl., 57 (2014), 761–794.
    [23] X. Y. Li, L. Gao, Q. K. Pan, et al., An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop, IEEE T. Syst. Man. Cy. S., 2018.
    [24] A. M. Bertram, Q. Zhang, S. C. Kong, et al., A novel particle swarm and genetic algorithm hybrid method for diesel engine performance optimization, Int. J. Engine. Res., 17 (2016), 732–747.
    [25] X. Y. Li, C. Lu, L. Gao, et al., An effective multi-objective algorithm for energy efficient scheduling in a real-life welding shop, IEEE T. Ind. Inform., 14 (2018), 5400–5409.
    [26] Y. N. Yan, L. Z. Liu, B. Y. Luo, et al., Arrangement of garment production line by particle swarm algorithm, J. Text. Res., 39 (2018), 120–124.
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