Rescheduling Plan Optimization of Underground Mine Haulage Equipment Based on Random Breakdown Simulation

Ning Li, Shuzhao Feng, Tao Lei, Haiwang Ye, Qizhou Wang, Liguan Wang and Mingtao Jia
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Ning Li: School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
Shuzhao Feng: School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
Tao Lei: School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
Haiwang Ye: School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
Qizhou Wang: School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
Liguan Wang: School of Resource and Safety Engineering, Central South University, Changsha 410083, China
Mingtao Jia: School of Resource and Safety Engineering, Central South University, Changsha 410083, China

Sustainability, 2022, vol. 14, issue 6, 1-18

Abstract: Due to production space and operating environment requirements, mine production equipment often breaks down, seriously affecting the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the amount of equipment, while the ore quality remained stable. Moreover, the improved optimization algorithm converged quickly and was characterized by high robustness.

Keywords: project scheduling; underground mine; random breakdown simulation; wolf colony algorithm; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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