Production scheduling plays a pivotal role in successfully executing any open-pit mining operation. It defines the most profitable extraction sequence of mineralized material from the ground subject to various physical and operational constraints. Different mathematical formulations have been proposed to achieve this goal; however, solving these models for real-sized deposits with multiple constraints is a challenging and computationally expensive task. Moreover, the inclusion of stockpiling option further complicates this task. The stockpile option adds flexibility to the operation by allowing excess low-grade ore storage for processing at a later stage when processing capacity is available. Accurate integration of stockpiling option in the production scheduling process through mathematical approaches leads to nonlinear constraints. This could further complicate the already challenging task since linear approximation of these nonlinear constraints could lead to sub-optimal results. Metaheuristic techniques could play an essential role in handling such situations. Though several attempts have been made to solve this problem through these techniques, little effort has been made to incorporate stockpiling option in the optimization process. This article presents a Simulated Annealing based approach for production scheduling of open-pit mines with stockpiling option. The proposed approach uses a stockpile and a greedy heuristic with a Simulated Annealing algorithm to achieve this goal. The greedy heuristic improves the Simulated Annealing algorithm's computational efficiency by managing its randomness. The proposed approach's performance and efficiency are demonstrated through three case studies (A, B, and C) under different algorithmic settings. Results of these case studies reveals that compared with the CPLEX solver, the proposed approach produced near optimal solution, within reasonable amount of time, proving the applicability of the proposed approach.

生产调度在成功执行任何露天采矿作业中起着至关重要的作用。它定义了在各种物理和操作约束下，从地下提取矿物质的最有利可图的顺序。已经提出了不同的数学公式来实现这一目标。但是，为具有多个约束条件的实际矿床求解这些模型是一项具有挑战性且计算量巨大的任务。此外，包含库存选项进一步使该任务复杂化。储备选件允许多余的低品位矿石存储，以便在有处理能力时在以后的阶段进行处理，从而增加了操作的灵活性。通过数学方法在生产调度过程中准确集成库存选项会导致非线性约束。由于这些非线性约束的线性逼近可能导致次优结果，因此这可能会使本已极具挑战性的任务进一步复杂化。元启发式技术可以在处理此类情况中发挥重要作用。尽管已尝试通过这些技术来解决此问题，但是在优化过程中几乎没有做出任何努力将库存选项纳入其中。本文提出了一种基于模拟退火的露天采矿生产计划方法，该方法具有库存选项。所提出的方法使用库存和贪婪启发式算法以及模拟退火算法来实现此目标。贪婪启发式通过管理随机性提高了模拟退火算法的计算效率。建议的方法” 通过在不同算法设置下的三个案例研究（A，B和C）证明了其性能和效率。这些案例研究的结果表明，与CPLEX求解器相比，该方法在合理的时间内产生了接近最佳的解决方案，证明了该方法的适用性。