Multi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling

Production scheduling and long-term planning are challenging in large-scale open pit mine operations. Proper planning ensures the maximum cash flow while utilizing mining resources. Most existing methodologies for solving open-pit mine scheduling problems are based on conventional approaches. However, these methods face many challenges in terms of computational cost due to the high dimensionality, and physical and operational constraints. Multi-objective evolutionary algorithms (MOEAs) have been successfully applied to a wide range of combinatorial optimization problems, as they often provide high-quality solutions to complex problems without significant design effort and computational cost. In this study, we investigate the effectiveness of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for the open-pit mine scheduling problem. We compare the effectiveness of this algorithm with the Global Simple Evolutionary Multi-Objective Optimizer (GSEMO) by analyzing well-known real-world mine deposits consisting of up to 112 687 blocks. We show that the NSGA-II algorithm has a clear advantage over GSEMO in obtaining better results. Further, we introduce the local search technique to enhance the performance of the NSGA-II.