The cut-off grade (COG) can be described as the lowest grade at which ore material will be economically justified for processing. Processing destinations refer to possible paths of the further processing of mined ore; they include options such as direct milling, heap leaching, stockpiling, or dumping into the waste rock pile. Creating a COG optimization model implies the development of the most profitable threshold level that would govern allocation of ore to the different destinations over time. In cases when a number of processing streams exist, COG optimization is of paramount importance for increasing the profitability of the whole mining project.
The underlying theory of modern COG optimization refers to the shift from static breakeven calculations to dynamic models. The static model establishes the grade needed for covering all mining and processing costs. Dynamic models, on the other hand, maximize the NPV of the mine over the lifetime of the project through the dynamic manipulation of COG in order to process high grade material in earlier periods for cash flow and send low grade material to alternative destinations.
The first thing that needs to be considered to develop a good model is the objective function, which must explicitly work on maximizing the net present value of the project subject to capacity constraints. In most cases, capacity constraints include the maximum amount of extraction, processing capacity, and refining capacity. An advanced model uses the mathematical approach to bring all the capacity constraints together. Models based on heuristics allow solving production scheduling problems effectively.
In case of more than one processing destination, the difficulty of the model increases due to the different metallurgical recoveries and costs for each stream. An optimization algorithm should always assess whether the ore block can generate more value being processed by a more costly mill having higher recovery or by a less costly heap leach pad with lower recovery. Modern strategic production planning involves using special multi-element cut-off grade algorithms that will help define proper routing (Cutler & Dimitrakopoulos, 2025).
In addition, the stochastic variables should be incorporated structurally in the optimization process, considering the existence of uncertainty in the real world. The sole use of deterministic models may lead to suboptimal destination policies owing to uncertainty regarding the geological supply of metal and market prices. With the incorporation of the simultaneous stochastic optimization model, the mathematical model would be able to change the order of production and cost of goods (COG), taking into account market fluctuations and stockpiling behavior (Paithankar et al., 2020). While popular in the case of the open-pit mines, such a robust optimization concept could also be used to analyze extraction limitations and different processing flows for complicated polymetallic underground deposits (Liu et al., 2023).
In conclusion, the development of the cut-off grade optimization model for several processing destinations requires a complex mathematical structure. With the development of a dynamic objective function, which is primarily focused on maximizing NPV and taking into account capacity constraints for each stream of processing, planners would be able to efficiently move the material. The inclusion of heuristic scheduling, multi-element consideration, and stochastic uncertainty makes the model economically beneficial and flexible enough for the volatile global mineral extraction industry.
References
Cutler, J., & Dimitrakopoulos, R. (2025). Optimising multi-element cut-off grades for a strategic production plan under geological uncertainty. International Journal of Mining, Reclamation and Environment, 1–15. https://doi.org/10.1080/17480930.2025.2455567
Khan, A., Asad, M. W. A., & Topal, E. (2023). A heuristic method for production scheduling of an open pit mining operation. International Journal of Mining, Reclamation and Environment, 38, 293–305. https://doi.org/10.1080/17480930.2023.2281201
Liu, D., Li, G., Hu, N., Xiu, G., & Ma, Z. (2023). Optimization of the cut-off grade for underground polymetallic mines. Gospodarka Surowcami Mineralnymi – Mineral Resources Management. https://doi.org/10.24425/gsm.2019.128198
Paithankar, A., Chatterjee, S., Goodfellow, R., & Asad, M. W. A. (2020). Simultaneous stochastic optimization of production sequence and dynamic cut-off grades in an open pit mining operation. Resources Policy, 66, 101634. https://doi.org/10.1016/j.resourpol.2020.101634
