This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.

本文提出了一种新颖的随机优化模型，该模型同时优化了露天采矿综合体中的短期提取顺序，铲子重定位，异构运输车队的调度以及提取材料的下游分配。所提出的随机优化公式除考虑与设备性能和卡车周期时间有关的不确定性外，还考虑了地质不确定性。该方法应用于现实世界的采矿综合体，强调了同时优化短期生产计划和车队管理的好处。与传统的两步法相比，在传统的两步法中，先优化生产计划，然后再优化采矿队的分配，铲子运动产生的成本减少了56％，铲子搬迁造成的生产损失减少了54％。此外，所需的卡车数量显示出更加平衡的状态，在年度计划范围内将卡车的总运营成本降低了3.1％，而在最繁忙的运输期内所需的运输能力降低了25％。元启发式求解方法用于在合理的时间范围内解决大型优化问题。