A Hybrid MIP-DES Framework for Open-Pit Mine Planning Using OPPS: Optimization and Simulation-Based Decision Support

Abstract

Optimizing mine planning maximizes economic value and efficiency by designing pit limits, scheduling extraction, allocating equipment, and managing costs. Traditional methods often fail to handle randomness and dynamic field conditions. To overcome these limitations, this research introduces a simulation-optimization framework integrating Mixed-Integer Programming (MIP) for pit limit design with discrete-event simulation (DES) to capture short-term operational variability. MIP indicates greater flexibility in incorporating complex constraints, resulting in a more realistic pit design. The geometry of open-pit layout growth is modeled using an Open Pit Production Simulator (OPPS), implemented in MATLAB and based on a modified elliptical frustum. The OPPS serves as a central tool, seamlessly linking the MIP-derived scheduling outputs with DES-based operational scenarios for realistic system evaluation. The simulator models equipment movements, haulage cycles, and scheduling under uncertain operational conditions. The framework dynamically interacts with a geological-economic block model to compute volumes of ore, waste, and stockpiles, while continuously tracking the Net Present Value (NPV). A case study on a 75,000-block iron ore deposit validates the framework. For 1,000 discrete-event simulation scenarios, the MIP-optimized pit design forms the foundation. At an annual discount rate of 8%, the best-performing scenario (OPPS) achieved an NPV deviation of -1.5%, maintained average ore grades of ~67%, and stripping ratios between 5.2–6.5, with a balanced annual production schedule of ~12 Mt ore and ~7 Mt waste over a 30-year my life. Overall, the proposed MIP-DES framework improves mine planning by combining optimization and simulation, enabling informed, long-term, and realistic operational decisions under uncertainty.