基于AHCP算法的大规模露天矿生产计划问题求解
作者:
中图分类号:

TD804

基金项目:

国家自然科学基金资助项目(51774228,51404182);陕西省自然科学基金资助项目(2017JM5043);陕西省教育厅专项科研计划项目(17JK0425)。


On the solution to large-scale open-pit mine production planning problem based on AHCP algorithm
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [26]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    随着露天矿生产计划问题规模的扩大,生产计划求解的难度急剧增加,传统求解方法难以在合理时间范围内获得高质量的解。针对以上问题,根据矿床开采过程中的特点,设计了一种具有惩罚的凝聚层次聚类算法(agglomerative hierarchical clustering algorithm with penalties,AHCP)与二进制入侵式杂草算法(binary intrusive weed algorithm,BIWO)相结合的方法来求解大规模露天矿生产计划问题。首先采用AHCP算法对块状矿床模型进行块体聚类处理,然后将聚合体作为对象建立01整数规划模型,并使用BIWO算法对其进行求解。实验结果表明,AHCP算法可以显著地提高BIWO算法求解大规模露天矿生产计划问题的能力。在保证解的质量的前提下,可将问题的整体求解时间缩短近90%。

    Abstract:

    With the expansion of the scale of open pit mines, the problems of preparing production plans has increased dramatically, leaving it difficult for traditional methods to obtain high-quality solutions in a reasonable time. In response to the above questions, a method combining agglomerative hierarchical clustering algorithm with penalties (AHCP) and binary intrusive weed algorithm (BIWO) is designed in this paper, according to the characteristics of mining, to solve the large-scale open pit mine production planning problem. Firstly, the block deposit model is aggregated according to AHCP algorithm. Then, the state of these units in each period is taken as variables to establish a 0-1 integer programming (IP) model. Finally, the IP model is solved by the BIWO algorithm. Experimental results show that AHCP algorithm can significantly improve the ability of BIWO algorithm in solving large-scale open pit production planning problems. The method in this paper can reduce the overall solution time by nearly 90% while ensuring the quality of the solution.

    参考文献
    [1] Saavedra-Rosas J, Jéivez E, Amaya J, et al. Optimizing open-pit block scheduling with exposed ore reserve[J]. Journal of the South African Institute of Mining and Metallurgy, 2016, 116(7):655-662.
    [2] 顾晓薇, 胥孝川, 王青, 等. 金属露天矿生产计划优化算法的改进[J]. 东北大学学报(自然科学版), 2014, 35(10):1492-1496.GU Xiaowei, XU Xiaochuan, WANG Qing, et al. Improving optimization algorithm of production scheduling for open-pit metal mine[J]. Journal of Northeastern University(Natural Science), 2014, 35(10):1492-1496. (in Chinese)
    [3] 王青, 顾晓薇, 胥孝川, 等. 露天矿生产规划要素整体优化方法及其应用[J]. 东北大学学报(自然科学版), 2014, 35(12):1796-1800.WANG Qing, GU Xiaowei, XU Xiaochuan, et al. Holistic optimization of production planning elements and its application for open-pit mine[J]. Journal of Northeastern University(Natural Science), 2014, 35(12):1796-1800. (in Chinese)
    [4] 黄俊歆, 郭小先, 王李管, 等. 一种新的用于编制露天矿生产计划开采模型[J]. 中南大学学报(自然科学版), 2011, 42(9):2819-2824.HUANG Junxin, GUO Xiaoxian, WANG Liguan, et al. A novel mining model for open-pit mine production scheduling[J]. Journal of Central South University(Science and Technology), 2011, 42(9):2819-2824. (in Chinese)
    [5] Rahmanpour M, Osanloo M. Determination of value at risk for long-term production planning in open pit mines in the presence of price uncertainty[J/OL]. Journal of the Southern African Institute of Mining and Metallurgy, 2016, 116(3)[2019-08-03]. http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2225-62532016000300007.
    [6] Hochbaum D S, Chen A. Performance analysis and best implementations of old and new algorithms for the open-pit mining problem[J]. Operations Research, 2000, 48(6):894-914.
    [7] Lambert W B, Newman A M. Tailored Lagrangian relaxation for the open pit block sequencing problem[J]. Annals of Operations Research, 2014, 222(1):419-438.
    [8] Ramazan S. The new fundamental tree algorithm for production scheduling of open pit mines[J]. European Journal of Operational Research, 2007, 177(2):1153-1166.
    [9] Askari-Nasab H, Pourrahimian Y, Ben-Awuah E, et al. Mixed integer linear programming formulations for open pit production scheduling[J]. Journal of Mining Science, 2011, 47(3):338-359.
    [10] Denby B, Schofield D. Open-pit design and scheduling by use of genetic algorithms[J]. Transactions of the Institution of Mining and Metallurgy, 1994, 103:A21-A6.
    [11] Kumral M, Dowd P A. A simulated annealing approach to mine production scheduling[J]. Journal of the Operational Research Society, 2005, 56(8):922-930.
    [12] Lamghari A, Dimitrakopoulos R. A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty[J]. European Journal of Operational Research, 2012, 222(3):642-652.
    [13] 胡乃联, 李勇, 李国清, 等. 用粒子群算法优化编制露天矿生产作业计划[J]. 北京科技大学学报, 2013, 35(4):537-543.HU Nailian, LI Yong, LI Guoqing, et al. Optimization of open-pit-mining operational planning by using a particle swarm algorithm[J]. Journal of University of Science and Technology Beijing, 2013, 35(4):537-543. (in Chinese)
    [14] Shishvan M S, Sattarvand J. Long term production planning of open pit mines by ant colony optimization[J]. European Journal of Operational Research, 2015, 240(3):825-836.
    [15] Khan A, Niemann-Delius C. A differential evolution based approach for the production scheduling of open pit mines with or without the condition of grade uncertainty[J]. Applied Soft Computing, 2018, 66:428-437.
    [16] Dósea M, Silva L, Silva M A, et al. Adaptive mean-linkage with penalty:a new algorithm for cluster analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2008, 94(1):1-8.
    [17] Miyamoto S, Terami A. Constrained agglomerative hierarchical clustering algorithms with penalties[C]//2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), June 27-30, 2011. Taipei, Taiwan, China. New York, USA:IEEE, 2011.
    [18] Wang Y Y, Zeng X J, Dong Z Y, et al. Stator single-phase-to-ground fault protection for bus-connected powerformers based on hierarchical clustering algorithm[J]. IEEE Transactions on Energy Conversion, 2013, 28(4):991-998.
    [19] Liu X, Huang G Y, Li C X, et al. Depressive effect of oxalic acid on titanaugite during ilmenite flotation[J]. Minerals Engineering, 2015, 79:62-67.
    [20] 李瑞, 胡乃联, 李国清, 等. 基于多目标0-1规划的采掘作业计划优化[J]. 金属矿山, 2017(2):102-108.LI Rui, HU Nailian, LI Guoqing, et al. Optimization of mining operation plan based on multi-objective 0-1 programming[J]. Metal Mine, 2017(2):102-108. (in Chinese)
    [21] 叶海旺, 欧阳枧, 李宁, 等. 矿山短期生产计划优化的多目标遗传粒子群算法[J]. 金属矿山, 2018(11):25-30.YE Haiwang, OUYANG Jian, LI Ning, et al. Multi-objective genetic particle swarm optimization algorithm for the short-term production planning in a mine[J]. Metal Mine, 2018(11):25-30. (in Chinese)
    [22] Mehrabian A R, Lucas C. A novel numerical optimization algorithm inspired from weed colonization[J]. Ecological Informatics, 2006, 1(4):355-366.
    [23] Shanti S K. Application of improved invasive weed optimization technique for optimally setting directional overcurrent relays in power systems[J]. Applied Soft Computing, 2019, 79:1-13.
    [24] Espinoza D, Goycoolea M, Moreno E, et al. MineLib:a library of open pit mining problems[J]. Annals of Operations Research, 2013, 206(1):93-114.
    [25] Osanloo M, Ataei M. Using equivalent grade factors to find the optimum cut-off grades of multiple metal deposits[J]. Minerals Engineering, 2003, 16(8):771-776.
    [26] Jélvez E, Morales N, Nancel-Penard P, et al. Aggregation heuristic for the open-pit block scheduling problem[J]. European Journal of Operational Research, 2016, 249(3):1169-1177.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

顾清华,李俊飞,卢才武.基于AHCP算法的大规模露天矿生产计划问题求解[J].重庆大学学报,2020,43(4):33-46.

复制
分享
文章指标
  • 点击次数:603
  • 下载次数: 818
  • HTML阅读次数: 1332
  • 引用次数: 0
历史
  • 收稿日期:2019-08-19
  • 在线发布日期: 2020-04-21
文章二维码