Which mathematical technique is employed to optimize the sequence of ore extraction to maximize net present value (NPV) considering time value of money?

The mathematical technique most commonly employed to optimize the sequence of ore extraction to maximize Net Present Value (NPV) while considering the time value of money is Linear Programming (LP). Net Present Value (NPV) represents the present value of future cash flows, both positive (revenue) and negative (costs), associated with a project. A higher NPV indicates a more profitable project. The time value of money acknowledges that money available today is worth more than the same amount in the future due to its potential earning capacity. Linear Programming is an optimization technique used to find the best possible solution to a problem, given a set of linear constraints. Linear constraints are mathematical inequalities or equalities that define the limitations on resources or operational parameters. In the context of mine planning, the objective function, which LP aims to maximize, is typically the NPV of the mining operation. The decision variables are the quantities of ore to be extracted from different blocks or areas within the orebody in each period (e.g., each year or quarter). The constraints represent various limitations, such as mining capacity, processing capacity, ore grade requirements, and precedence relationships. Precedence relationships dictate the order in which blocks can be mined; for example, a block might not be accessible until overlying blocks are removed. LP models for mine scheduling often include a discount rate to reflect the time value of money. This discount rate reduces the value of future cash flows, encouraging the extraction of higher-value ore earlier in the mine life to maximize NPV. For example, consider two ore blocks with the same total ore content. LP will typically favor extracting the block that can be mined earlier because the revenue generated from that block will have a higher present value due to the discounting of future cash flows. While other optimization techniques exist, such as dynamic programming and mixed-integer programming, Linear Programming offers a good balance between solution speed and model complexity for large-scale mine scheduling problems. It allows for efficient evaluation of numerous extraction sequences to identify the schedule that yields the highest NPV. More advanced techniques like Mixed-Integer Programming are used when binary decision variables (e.g., whether to mine a block or not) are essential, but they come with increased computational cost.