Optimization of ore production scheduling strategy using NSGA-II-GRA in open-pit mining

To address the challenges posed by ore grade fluctuations, extraction cost variations, and market price instability in open-pit ore production strategies, an optimization model integrating a solution algorithm integrating dynamic simulation is proposed. First, Monte Carlo simulation is used to generate random variables, simulating ore grade and extraction cost fluctuations at different extraction points. Then, the Non-dominated Sorting Genetic Algorithm II with Grey Relational Analysis (NSGA-II-GRA) is employed for an initial solution to obtain the preliminary Pareto-optimal solution set. Furthermore, a market price fluctuation constraint equation is introduced during the optimization process to conduct a second iterative optimization of the initial solution set, generating optimized plans under different price fluctuation intervals. Finally, by integrating subjective and objective weights, the weighted grey relational analysis method is applied to select the optimal Ore Production Scheduling Strategy. The optimization results indicate that, while achieving the optimization objectives, calcium oxide grade and production target achievement rate increased by 7% and 7.17%, respectively, while ore output increased by 16.7% and 20.5% under different price fluctuation intervals.