大型露天金属矿山全寿期采剥计划优化方法研究与应用OA

To address the challenge of excessive decision variables caused by the enormous number of value blocks in life-of-mine min-ing and stripping planning for large open-pit metal mines,this study proposes a value block optimization method based on improved K-means clustering.By aggregating value blocks with similar attributes and spatial proximity into value clusters that satisfy spatial connec-tivity constraints and avoid geometric anomalies,the number of decision variables is significantly reduced.In a case study of a large open-pit copper-molybdenum mine,the number of clusters was only 16.13%of the original value blocks.Building upon this,a 0-1 integer programming model was formulated with the objective of maximizing net present value(NPV),incorporating multiple con-straints such as mining capacity,mineral processing capacity,metal grade fluctuations,concurrent mining bench limits,extraction se-quence,and stripping ratio.The research was implemented using the digital mining software platform DIMINE.Optimization results demonstrated maximized mine NPV while maintaining a stable and reasonable stripping ratio(2.01~2.13),validating the feasibility and effectiveness of the proposed method.