解决终极坑限问题的推重标签算法的最高级实现

IF 1.1 Q3 MINING & MINERAL PROCESSING Journal of Mining and Environment Pub Date : 2021-03-15 DOI:10.22044/JME.2021.10481.1999
Mehdi Talaei, Amin Mousavi, A. Sayadi
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引用次数: 0

摘要

目前,由于经济和地质不确定性的存在,以及基于场景的项目评估在露天矿设计中的应用越来越多,有必要找到一种能够在短时间内确定最终矿坑极限的精确算法。最终矿坑边界的确定是一个重要的优化问题,它决定了最终将从地下开采出什么,并直接影响到开采成本、收益、采矿设备的选择以及矿坑外地面基础设施的近似。在优先关系(访问)约束下,求解非贴现利润最大化问题。本文讨论了推重标算法的最高级推重标(HI-PR)实现,并将其应用于解决最终坑限优化问题。HI-PR使用最高标签选择规则、全局更新和间隙启发式来减少计算量。提出的算法是为了解决Minelib网站上公开的九个现实生活基准案例研究的最终坑限制。结果表明,与现有算法相比,该算法可以在更短的计算时间内得到最优解。对于包含112687块和3,035,483个约束的最大数据集,算法100次运行的平均求解时间为4 s,而IBM CPLEX作为精确求解器,在24小时内无法找到任何可行的解。这种加速能力可以大大改善当前在实时矿山规划和协调方面的挑战,在这些方面需要快速可靠的解决方案。
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Highest-Level Implementation of the Push–Relabel Algorithm to Solve the Ultimate Pit Limit Problem
Nowadays due to the existence of the economic and geological uncertainties and the increasing use of scenario-based project evaluation in the design of open-pit mines, it is necessary to find an exact algorithm that can determine the ultimate pit limit in a short period of time. Determining the ultimate pit limit is an important optimization problem that is solved to define what will be eventually extracted from the ground, and directly impacts the mining costs, revenue, choosing mining equipment, and approximation of surface infrastructures outside the pit. This problem is solved in order to maximize the non-discounted profit under the precedence relation (access) constraints. In this paper, the Highest-Level Push-Relabel (HI-PR) implementation of the push–relabel algorithm is discussed and applied in order to solve the ultimate pit limit optimization problem. HI-PR uses the highest-label selection rule, global update, and gap heuristics to reduce the computations. The proposed algorithm is implemented to solve the ultimate pit limit for the nine real-life benchmark case study publicly available on the Minelib website. The results obtained show that the HI-PR algorithm can reach the optimum solution in a less computational time than the currently implemented algorithms. For the largest dataset, which includes 112687 blocks and 3,035,483 constraints, the average solution time in 100 runs of the algorithm is 4 s, while IBM CPLEX, as an exact solver, could not find any feasible solution in 24 hours. This speeding-up capability can significantly improve the current challenges in the real-time mine planning and reconciliation, where fast and reliable solutions are required.
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
发文量
0
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