Integrated optimization model for production and equipment dispatching in underground mines

Integrated optimization of production plays a key role in realizing short completion times of underground mine processes. To achieve the best production operation, a production plan should consider factors such as the working time and equipment capacity to arrange stope mining operations, equipment, and materials supply. Underground mining operation is a process with multiple working sites and multiple cycle operations, which include rock drilling, blasting, ventilation, supporting, ore-transportation, and backfilling. Once the stope mining begins, the above six steps must be intensively integrated to minimize the exposure time. Each step of stope mining has a significant effect on the whole mining plan; therefore, it is very important to form a reasonable configuration to achieve the mining plan target. The most difficult part of underground mining operation is how to organize the production process with limited resources (i. e., equipment and materials) to accomplish a large production task. In this paper, an integrated optimization model was described for compacting the integrated production process and efficiently dispatching production equipment. Two optimal objectives were combined to shorten the interval between production processes and overall working time. Production factors such as production cycle, type of operation equipment, and production capacity are analyzed in a mathematical model. In addition, the production safety was also considered and the time intervals between different working processes were considered to shorten the stope uncontrolled time to keep the production safety in the mathematical model. The best result of mining sequence and equipment dispatching was obtained by an improved genetic algorithm which searches the feasible solutions through primary-secondary two-step searching method. The model was validated in a large gold mine in China to work out the optimal equipment scheduling plan. The results show that compared with the traditional single-target optimization, the mining task can be accomplished effectively and the intervals between processes are minimized to improve safety in the mining operation.