Heuristic and Exact Approaches to Optimize the Production Scheduling of Mines Transitioning from the Open-Pit to Block Caving

Mine planning engineers perform production schedule optimization to determine the time sequence in which ore blocks should be extracted to maximize value. For mines transitioning from a surface mining method to an underground extraction method (transition mines), the production schedule optimization is complex with no applicable solutions. We present two optimization approaches for the transition mine production scheduling problem (TMPSP)—a disintegrated heuristic approach and an integrated exact approach—and investigate the conditions in which an integrated approach to the TMPSP is superior to a disintegrated approach. Mixed-integer linear programming (MILP) models are developed to optimize the production schedule per period and crown pillar placement for the TMPSP. The MILP is implemented in Python and solved with the Gurobi® Optimizer. A case study is performed to validate the models, with a comparative analysis to obtain operational insights. The computational results show that the integrated model achieves 5% higher NPV than the disintegrated approach with less computational effort.