基于AHCP算法的大规模露天矿生产计划问题求解

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0-1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%。

On the solution to large-scale open-pit mine production planning problem based on AHCP algorithm

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.