基于启发式遗传算法的地下采场作业计划优化模型

doi:10.11872/j.issn.1005-2518.2023.04.023

Abstract

Abstract:

With the rapid development of digital economy in the world，how to realize the rapid optimal allocation of underground mine production equipment has become the key to the continuous advancement and in-depth application of digital mine.In view of the characteristics of underground mines such as limited space，limited equipment resources，and large production tasks，an optimization model was constructed for production planning of the follow-up filling mining method in the open pit using pre-controlled roof medium-and deep-hole and sublevel open-stopping and subsequent filling method.The model aims at minimizing the interval time between adjacent processes and the total production time，and the above issue is solved using genetic algorithms.The genetic algorithms used for solving the problem include traditional genetic algorithms and optimized genetic algorithms.Taking the actual data of a copper mine test stope in Zambia as an example，it can be seen from the iterative results that all genetic algorithms can solve the model，and the optimized genetic algorithm converges faster than the ordinary genetic algorithm.The genetic algorithm accelerated by heuristic algorithm has the fastest convergence speed.Therefore，the heuristic genetic algorithm is used to solve the multi-objective optimization model and the results are visualized.After analyzing the solution results，it is found that the average utilization rate of equipment is only 49.16%，and the utilization rate of some equipment is low，so the number of equipment is optimized.After the number of equipment was optimized and solved again，the average utilization rate of mine equipment increased to 64.8%，basically meeting the requirements of the mine.In terms of production，the daily average ore output is 3 631.19 t/d，which meets the mining demand and effectively shorts the operation time interval to ensure the requirements of mining safety.In addition，copper and cobalt sunrise ore grade fluctuation is small，easy to concentrate.Therefore，the algorithm and model can quickly and effectively solve the problem of multi-equipment coordination in a copper mine in Zambia，improve production efficiency and safe mining.

Key words: underground metal mines, sublevel open-stoping method, subsequent filling, multi-objective optimization model, heuristic algorithm, fenetic algorithm, equipment optimization

CLC Number:

TD85-9

Shuang HUANG, Mingtao JIA, Fang LU. Optimization Model of Underground Stope Working Plan Based on Heuristic Genetic Algorithm[J].Gold Science and Technology, 2023, 31(4): 669-679.

Figures/Tables 15

Fig.1

Fig.2

Table 1

Parameters for multi-objective optimization model"

参数	参数含义
$M A n$	盘区编号
$O n$	矿带编号
$S n$	采场编号
$P n$	工序编号
$α$	设备工作能力折减系数， $α ∈ [0.80,0.85]$
$β$	用于确定工作 $j$ 开始工作时间的变量
$S T j$	工作 $j$ 开始工作时间
$O T j$	工作 $j$ 作业时间，包含设备移动到采场工作面的时间和设备在工作面工作的时间
$B w$	工作 $j$ 作业期间包含爆破窗口的个数
$C T j$	工作 $j$ 作业结束时间
$S T m j$	设备 $m$ 可以在工作 $j$ 开始工作的最早时间
$M S o$	矿带 $O n$ 开采状态， $M S o ? = 0$ 表示矿带 $O n$ 未开采， $M S o ? = 1$ 表示矿带 $O n$ 已开采
$S T o$	矿带 $O n$ 开始开采的时间， $S T o ? = ? S T j, j = [? M A n, O n, 1,1]$
$C T o$	矿带 $O n$ 结束开采的时间，若矿带未开采，则 $C T o = 0$ ，若矿带已开采，则 $C T o = C T j, j = [? M A n, O n, S n, 7]$
$T S o$	矿带 $O n$ 工序时间间隔

Table 1

Table 2

Datasets of multi-objective optimization model"

数据集	数据集含义
j	工作编号集合， $j = [? M A n, O n, S n, ? P n]$
$J p i$	采场工序生产信息集合，包括工序工作量，位置坐标等
$M p i$	设备生产信息集合，包括设备生产能力，移动速度等
$J w i$	采场工序工作信息集合，包括工序使用设备的编号，开始工作时间，结束工作时间等
$M w i$	设备工作信息集合，包括设备开始工作时间，结束工作时间，工作地点等
$O w i$	所有矿带工作信息集合，包括该矿带开采状态，开始开采时间，结束开采时间等

Table 2

Table 3

Fig.3

Fig.4

Fig.5

Table 4

Table 5

Fig.6

Fig.7

Fig.8

Fig.9

Table 6

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目标函数	目标函数含义
C₁	染色体最大完工时间
C₂	染色体总工序时间间隔

设备	利用率/%
凿岩设备	28.4
装药设备	83.5
撬毛支护设备	51.3
出矿设备	26.4
充填设备	56.2

设备名称	利用率/%
凿岩设备	61.6
装药设备	81.7
撬毛支护设备	66.8
出矿设备	57.9
充填设备	56.0

工作编号	工作开始时间	工作结束时间	工作设备	工作出矿量/t	工作铜出矿品位/%	工作钴出矿品位/%
1050101	0：00	1：28	凿岩台车2	0.00	0.00	0.00
5050101	0：00	7：37	凿岩台车3	0.00	0.00	0.00
5010101	0：00	8：00	凿岩台车1	0.00	0.00	0.00
1100102	0：00	8：00	装药台车1	0.00	0.00	0.00
3040105	0：00	4：08	铲运机1	654.13	2.04	0.17
4030105	0：00	5：23	铲运机2	933.45	2.00	0.13
2060101	1：28	8：00	凿岩台车2	0.00	0.00	0.00
5050102	7：37	8：00	装药台车2	0.00	0.00	0.00
6010101	7：37	8：00	凿岩台车3	0.00	0.00	0.00

Optimization Model of Underground Stope Working Plan Based on Heuristic Genetic Algorithm

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