Optimization of Trackless Equipment Scheduling in Underground Mines Using Genetic Algorithms
AffiliationUniv Arizona, Mine Intelligence Res Grp, Dept Min & Geol Engn
Genetic algorithm (GA)
MetadataShow full item record
CitationWang, H., Tenorio, V., Li, G. et al. Optimization of Trackless Equipment Scheduling in Underground Mines Using Genetic Algorithms. Mining, Metallurgy & Exploration (2020). https://doi.org/10.1007/s42461-020-00285-8
JournalMINING METALLURGY & EXPLORATION
RightsCopyright © Society for Mining, Metallurgy & Exploration Inc. 2020.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractThis paper presents an algorithm for optimizing the scheduling of trackless equipment in underground mines. With the shortest working interval and maximum productivity as goals, a genetic algorithm (GA) is used to solve the problem, and obtain the optimal working sequence with the most suitable equipment configuration possible. The input for the proposed method is the number of units and capacity of trackless equipment, the production process, ore amount in stopes, and the distance between stopes. The algorithm is verified using four setups of 5 stopes with 5 cycles, 5 stopes with 15 cycles, 10 stopes with 10 cycles, and 10 stopes with 30 cycles. The solution time of the algorithm is no more than 20 min, which is acceptable for practical applications. The results show that the setup of 10 stopes with 30 cycles is closer to the actual production of the mines, and the optimization model can effectively improve the operation efficiency. In this scenario, the robustness of the optimization is tested by simulating equipment failure events. Under the condition of 8% failure rate, the operation time is extended over 3.21-14.56% than expected, which represents strong robustness. The algorithm can quickly provide a feasible and effective solution for the production scheduling decision of trackless equipment in underground mines.
Note12 month embargo; published: 12 August 2020
VersionFinal accepted manuscript
SponsorsNational Natural Science Foundation of China