Mine extraction sequencing through fuzzy optimization
Mine extraction is scheduled under many technical and financial uncertainties leading to robustness and reliability problems. Block grades, block ore and waste tonnages, ore prices, mining and mineral processing costs, discount rates, recoveries and slopes are imprecise variables because of sparse data and unknown future events. Main idea behind the approach herein is to treat the coefficients of the objective function and some constraints as imprecise data. The problem expressed in fuzzy form is first converted to a multi-objective optimization problem, which maximizes the most possible value of the imprecise net present value (NPV) of mining venture, minimizes the risk of generating lower NPV and maximizes the possibility of generating higher NPV simultaneously. In this process, positive and negative ideal solutions are generated. The model is then formulated in a single objective mixed integer programming model as maximin (maximize the minimum) operation. In order to demonstrate the proposed model, a case study was conducted. The findings showed that the model generated an efficient mine production scheduling compromise solution.
decision making under uncertainty,mine production scheduling,fuzzy programming