摘要
One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit mines.Deterministic and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this problem.Within the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling problem.Although the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational costs.In the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade uncertainty.It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence.Moreover,the Lagrangian coefficients are updated by using PSO and BA.The presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian approach.The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution.Hence,it is more effectual than the conventional method.Furthermore,the time and cost of computation are diminished by the proposed hybrid strategies.The CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.
露天采矿工艺是地表采矿的一种方法,通过开挖坑洞从地表向下开采矿石或废物。工业生产过程中,露天矿的长期生产调度(LTPS)问题是最大的生产难题之一,而基于确定性方法和不确定性的方法被认为是解决此类问题的主要策略。在过去几年中,许多研究人员充分探究了一种成本较低的新型计算法,即元启发式方法,用以解决矿山设计和生产调度问题。该方法尽管无法保证最终方案的最优性,但能够以相对较低的计算成本推算出足够优秀的解决方案。本文提出了增强拉格朗日松弛(ALR)与粒子群优化(PSO),以及ALR和蝙蝠算法(BA)的两种混合算法模型,以解决不确定品位条件下的露天矿生产调度问题。该混合模型采用ALR方法解决露天矿生产调度问题,以提高其计算性能并加快收敛速度,并通过PSO或BA更新拉格朗日系数。所提出的计算模型与ALR遗传算法、ALR传统次梯度法和常规方法(未使用拉格朗日方法)的计算结果进行了比较,结果表明:相比于常规方法,ALR法可以更加有效地解决大规模问题,并提出合理的解决方案。此外,混合算法可以降低计算时间和成本,ALR-BA方法的CPU运算时间比ALR-PSO方法大约高7.4%。
