Volume 48 Issue 6
Jun.  2022
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LI Zaiyou, SUN Yanbin, WANG Xiaoguang, et al. Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm[J]. Journal of Mine Automation，2022，48(6)：87-94, 127. DOI: 10.13272/j.issn.1671-251x.17929
Citation: LI Zaiyou, SUN Yanbin, WANG Xiaoguang, et al. Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm[J]. Journal of Mine Automation，2022，48(6)：87-94, 127. DOI: 10.13272/j.issn.1671-251x.17929
shu

Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm

  • 1.

    Shenhua Beidian-Shengli Energy Co., Ltd., Xilinhot 026000, China

  • 2.

    College of Mines, Liaoning Technical University, Fuxin 123000, China

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  • Received Date: April 11, 2022
  • Revised Date: June 19, 2022
  • Available Online: June 27, 2022
    Graphical Abstract
    • Abstract
  • In order to solve the problem of unmanned truck transportation scheduling in open-pit mines, the minimum sum of fuel cost, fixed start-up cost, breakdown maintenance cost, and network base station construction and maintenance cost are taken as the objective functions. The mining amount of mining station, crushing amount of crushing station, truck number and truck transportation workload are taken as the constraint conditions. The optimization model of unmanned truck transportation scheduling in open-pit mines is established. To solve the problem of imbalance between global exploration and local mining ability in the tunicate swarm algorithm, an improved tunicate swarm algorithm (ITSA) based on Singer mapping and adaptive updating mechanism of parameter position is proposed. And it is applied to solve the optimization model of unmanned truck transportation scheduling in open-pit mines. Singer mapping is introduced to enhance the distribution of the initial tunicate swarm in the solution space and accelerate the compression of the solution space, thus improving the convergence speed of the algorithm. Through the adaptive updating mechanism of parameter position, the positions of the tunicate and the optimal tunicate are adjusted to increase the search range of the solution space. Therefore, the algorithm jumps out of the local optimization. The simulation results show that ITSA has better convergence precision, convergence speed and stability compared with the four population intelligent optimization algorithms of grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA), atom search optimization algorithm (ASO) and tunicate swarm algorithm (TSA). In the unimodal benchmark function, the evaluation indexes of ITSA are far better than those of the other four algorithms, which shows that ITSA has better local mining capacity. In the multi-peak benchmark function, the evaluation indexes of ITSA show better optimization performance, which indicates that ITSA has better global exploration performance. The practical application scenario verification shows that ITSA has faster convergence speed and higher convergence precision when used for solving the unmanned truck transportation scheduling optimization model. And ITSA reduces the truck transportation cost and transportation distance.
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    • References (22)
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