The annual production scheduling of open pit mines determines an optimal sequence for annually extracting the mineralized material from the ground. The objective of the optimization process is usually to maximize the total Net Present Value (NPV) of the operation. Production scheduling is typically a Mixed Integer Programming (MIP) type problem containing uncertainty in the geologic input data and economic parameters involved. Major uncertainty affecting optimization is uncertainty in the mineralized materials (resource) available in the ground which constitutes an uncertain supply for mine production scheduling.A new optimization model is developed herein based on two-stage Stochastic Integer Programming (SIP) to integrate uncertain supply to optimization; past optimization methods assume certainty in the supply from the mineral resource. As input, the SIP model utilizes a set of multiple, stochastically simulated scenarios of the mineralized materials in the ground. This set of multiple, equally probable scenarios describes the uncertainty in the mineral resource available in the ground, and allows the proposed model to generate a single optimum production schedule.The method is applied for optimizing the annual production scheduling at a gold mine in Australia and benchmarked against a traditional scheduling method using the traditional single "average type" assessment of the mineral resource in the ground. In the case study presented herein, the schedule generated using the proposed SIP model resulted in approximately 10% higher NPV than the schedule derived from the traditional approach.S. Ramazan ( )

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The production scheduling of open pit mines is an intricate, complex and difficult problem to address due to its large scale and the unavailability of a truly optimal net present value (NPV) solution, as well as the uncertainty in key parameters involved. These key factors are geological and mining, financial and environmental. Geological uncertainty is a major contributor in failing to meet production targets and the financial expectations of a project especially in the early stages of a project. Stochastic integer programming (SIP) models provide a framework for optimising mine production scheduling considering uncertainty. A specific SIP formulation is shown herein that generates the optimal production schedule using equally probable simulated orebody models as input, without averaging the related grades. The optimal production schedule is then the schedule that can produce the maximum achievable discounted total value from the project, given the available orebody uncertainty described through a set of stochastically simulated orebody models. The proposed SIP model allows the management of geological risk in terms of not meeting planned targets during actual operation, unlike the traditional scheduling methods that use a single orebody model and where risk is randomly distributed between production periods while there is no control over the magnitude of the risks on the schedule. Notably, the testing of the SIP formulation in two cases, a gold and a copper deposit, shows that the expected total NPV of the schedule using the SIP approach is significantly higher (10 and 25% respectively) than the traditional schedule developed using a single estimated orebody model.

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