GHG-considerate operational excellence in surface mines: an integrated stochastic discrete-continuous simulation-based optimization framework

Surface mines supply more than 96% of the raw minerals used across industrial sectors, making its transportation systems a critical component of operational efficiency and environmental performance. This study develops an integrated stochastic discrete–continuous simulation-based optimization framework for transportation decision-making in surface mining operations. The framework couples a mixed-integer linear programming (MILP) optimization model with a discrete–continuous simulation model to evaluate system performance under operational uncertainty. The proposed approach simultaneously considers economic and environmental objectives by minimizing transportation costs while reducing greenhouse gas (GHG) emissions. The framework is applied to an operating surface copper mine to determine the optimal size of the transportation fleet. Results demonstrate that the integrated simulation–optimization approach improves transporter waiting time at loading points by 33%, leading to a 3.5% increase in production. In addition, the framework enables significant environmental benefits, achieving a 72% reduction in carbon dioxide emissions. Sensitivity analysis is conducted to evaluate the influence of key operational parameters on system performance and to examine trade-offs between cost efficiency and emission reduction.