A Network Flow Model for Operational Planning in an Underground Gold Mine

Q4 Energy
Suliman Emdini Gliwan, Kevin Crowe
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引用次数: 0

Abstract

In underground mines, the problem of efficiently scheduling and allocating weekly operations has a major impact on the long-term productivity of the mine. The problem of selecting the optimal locations for operations in an underground gold mine is a complex task. It is not solved by simply selecting the levels with the richest grade because the transportation network for ore in an underground mine has a diverse set of capacity constraints that can frustrate immediate mining of all the richest levels. To solve this scheduling difficulty, we formulated a new mixed-integer network flow model of the problem of weekly allocating mining operations in an underground goldmine such that the total gold mined (in ounces) was maximized subject transportation capacity constraints. The model was applied an underground gold mine in Red Lake, Ontario, Canada. The results were compared to those of a two greedy heuristic models that were designed to represent the decision-making heuristics that are currently used at the mine. It was found that the new model yielded solutions that improved upon the two greedy heuristics by 14.7% and 6.0%, respectively. The results of this research illustrate that the development of this optimization model can support decisions to improve a gold mine’s productivity.
地下金矿作业规划的网络流模型
在地下矿山中,周作业的有效调度和分配问题对矿山的长期生产效率有着重要的影响。地下金矿开采的最佳选址问题是一个复杂的问题。这不是简单地通过选择品位最丰富的矿层来解决的,因为地下矿山的矿石运输网络具有各种各样的容量限制,这些限制可能会阻碍所有最丰富矿层的立即开采。为了解决这一调度难题,在运输能力约束下,建立了一个新的地下金矿开采作业周分配问题的混合整数网络流模型,使开采总金量(盎司)最大化。该模型应用于加拿大安大略省红湖地下金矿。结果与两个贪婪启发式模型的结果进行了比较,这两个模型被设计用来表示目前在矿山使用的决策启发式。结果发现,新模型的求解结果比两种贪心启发式算法分别提高了14.7%和6.0%。研究结果表明,该优化模型的建立可以为提高金矿生产效率的决策提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Mines, Metals and Fuels
Journal of Mines, Metals and Fuels Energy-Fuel Technology
CiteScore
0.20
自引率
0.00%
发文量
101
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