Transporting ore from mines to ports is of significant interest in mining supply chains. These operations are commonly associated with growing costs and a lack of resources. Large mining companies are interested in optimally allocating their resources to reduce operational costs. This problem has been previously investigated in the literature as resource constrained job scheduling (RCJS). While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention. RCJS with uncertainty is a hard combinatorial optimisation problem that cannot be solved efficiently with existing optimisation methods. This study proposes an adaptive population-based simulated annealing algorithm that can overcome the limitations of existing methods for RCJS with uncertainty including the premature convergence, the excessive number of hyper-parameters, and the inefficiency in coping with different uncertainty levels. This new algorithm is designed to effectively balance exploration and exploitation, by using a population, modifying the cooling schedule in the Metropolis-Hastings algorithm, and using an adaptive mechanism to select perturbation operators. The results show that the proposed algorithm outperforms existing methods across a wide range of benchmark RCJS instances and uncertainty levels. Moreover, new best known solutions are discovered for all but one problem instance across all uncertainty levels.
翻译:从矿山向港口运输矿石对于采矿供应链的兴趣很大,这些作业通常与成本增加和资源缺乏有关。大型采矿公司有兴趣优化资源配置,以减少运营成本。这个问题曾作为资源有限的工作时间安排问题在文献中调查过。虽然提出了若干优化方法以解决确定性问题,但与资源可得性有关的不确定性,采矿作业中不可避免的挑战,却未受到足够重视。具有不确定性的RCJS是一个硬组合优化问题,无法通过现有优化方法有效解决。这项研究提出了适应性的人口模拟肛门算法,可以克服现有方法的局限性,而现有方法的不确定性包括过早趋同、超光度计数量和应对不同不确定性水平的效率不高。这一新算法的目的是通过使用人口、修改Metopolis-Hastings算法中的冷却计划以及使用适应机制选择过敏操作者,有效平衡勘探和开采的不确定性。结果显示,拟议的基于人口模拟模拟模拟算法超越了所有已知的不确定性水平的当前标准。