optimizing long-term production scheduling in open pit mining under commodity price uncertainty: a two-stage stochastic programming approach

In the context of open pit mining operations, long-term production scheduling faces significant challenges due to inherent uncertainties, particularly in commodity prices. traditional mathematical models often adopt a single-point estimation strategy for commodity price, leading to suboptimal mine plans and missed production targets. the simultaneous effect of commodity price uncertainty on the cut-off grade and long-term production scheduling is less considered. this paper introduces a novel model for optimizing open pit mine long-term production scheduling under commodity price uncertainty considering a dynamic cut-off grade strategy, based on a two-stage stochastic production programming (spp) framework. the presented model seeks to identify optimal mining block sequences, maximizing total discounted cash flow while penalizing deviations from production targets. to illustrate the model’s efficiency, it was implemented in a copper mine. first, the geometric brownian motion (gbm) model is used to quantify the future commodity price. then, both deterministic and spp models were solved using gams software. the results showed that the practical npv obtained from the spp model is approximately 3% higher than the dpp model, while all constraints are satisfied.