An open-source optimization model for sustainable open-pit mine production scheduling

Open-pit mine production scheduling is a complex optimization problem that requires balancing economic performance with operational feasibility and environmental responsibility. While significant advances have been made in mathematical formulations, many existing approaches remain computationally demanding, lack transparency, or are implemented within proprietary systems that limit reproducibility. This study presents a fully reproducible, open-source Python-based optimization framework for open-pit production scheduling that integrates sustainability considerations directly into decision-making. The model employs a mixed-integer "by" formulation with three-dimensional cone-based precedence constraints and incorporates environmental penalties into block valuations to enable ESG-aligned optimization. The framework is designed to be dataset-agnostic and extensible, supporting benchmarking and adaptation to different mining scenarios. The approach is demonstrated using a synthetic block model of 602 blocks over a 12-period planning horizon. The optimization achieved a discounted Net Present Value (NPV) of approximately USD 674.83 million while strictly satisfying precedence, capacity, and operational constraints. Results show that the integration of environmental penalties influences block selection and extraction sequencing, enabling more sustainable scheduling outcomes with only a moderate reduction in economic performance. Sensitivity analyses further confirm the robustness of the framework under varying economic and operational conditions. The study demonstrates that open-source optimization can provide a transparent, adaptable, and effective alternative to proprietary mine planning tools. By combining reproducibility, sustainability integration, and computational efficiency, the proposed framework establishes a practical baseline for future research and large-scale applications in sustainable mine planning.