Stope sequencing optimization for underground mines through chance-constrained programming

Economic evaluations of underground mining operations are made based on the estimated or simulated grades of the block model as the actual values are not known. Depending on how the planning decisions are made, the risk of the operation can be managed and decreased. This research introduces a mixed-integer linear programming (MILP) model for stope sequencing optimization that considers the uncertainty associated with net present value (NPV) using chance-constrained programming (CCP). The introduced method permits achieving a balance between maximizing NPV and reducing the associated operational risk through transforming the problem into a multiobjective problem, where the objectives are maximizing the NPV and minimizing the risk. This risk is expressed in terms of the standard deviation of project outcomes, taking into account multiple possible scenarios. In a case study, the efficiency of the method was demonstrated as a decision-making tool capable of translating a specified risk or reliability level into a stope sequencing plan.