Abstract: Mathematical Optimization of Transition Mines: Models and Algorithms for Sustainable Scheduling
Mine planning engineers optimize production schedules to maximize economic value, particularly for mines transitioning from surface to underground extraction (transition mines). Transition mine production scheduling (TMPSP) poses significant computational challenges. This research evaluates three optimization approaches: a disintegrated heuristic model, an integrated exact model, and advanced algorithms including Benders’ decomposition combined with the Bienstock-Zuckerberg (BZ) algorithm. Mixed-integer linear programming (MILP) models, implemented in Python with Gurobi optimizer, were developed to optimize extraction sequences, crown pillar placements, and transition depths. This research underscores the importance of integrating realistic operational constraints and employing sophisticated algorithms to enhance economic outcomes for transition mine operations.