Paper
8 June 2024 Coordinated scheduling optimisation strategy of mining equipment in underground coal mines
Tianyan Liu, Biao Wang, Hanzhao Liu, Bicheng Tang, Ji Ke, Changqing Wang, Aijun Li, Zhigang Ren
Author Affiliations +
Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 131711F (2024) https://doi.org/10.1117/12.3032028
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China
Abstract
Aiming at the scheduling problem of underground mining equipment in shot mining, this paper proposes an improved cultural gene algorithm (MA). The global search applies the genetic algorithm, and some adjustments are made in its crossover and mutation operations; the local search uses the simulated annealing algorithm. The global search applies the genetic algorithm, and some adjustments are made in its crossover and mutation operations; the local search uses the simulated annealing algorithm, considering that the algorithm will have a certain probability to jump out of the optimal solution range, so on the basis of the original algorithm, the Gaussian function is replaced by the Cauchy function to avoid this problem. The algorithm is applied to the scenario of 5S15J for simulation experiments. After that, compared with the results of the genetic algorithm, it shows that the improved MA algorithm is obviously better in total time and total interval time, and can obtain high-quality solutions and an ideal cooperative scheduling strategy.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Tianyan Liu, Biao Wang, Hanzhao Liu, Bicheng Tang, Ji Ke, Changqing Wang, Aijun Li, and Zhigang Ren "Coordinated scheduling optimisation strategy of mining equipment in underground coal mines", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 131711F (8 June 2024); https://doi.org/10.1117/12.3032028
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KEYWORDS
Mining

Genetic algorithms

Mathematical optimization

Algorithms

Computer simulations

Detection and tracking algorithms

Algorithm development

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