Browsing by Author "Flores, Ignacio Ortiz"

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    Heuristic and Exact Approaches to Optimize the Production Scheduling of Mines Transitioning from the Open-Pit to Block Caving
    (2024) Anani, Angelina; Flores, Ignacio Ortiz; Li, Haitao; Jalilzadeh, Afrooz
    Mine planning engineers perform production schedule optimization to determine the time sequence in which ore blocks should be extracted to maximize value. For mines transitioning from a surface mining method to an underground extraction method (transition mines), the production schedule optimization is complex with no applicable solutions. We present two optimization approaches for the transition mine production scheduling problem (TMPSP)-a disintegrated heuristic approach and an integrated exact approach-and investigate the conditions in which an integrated approach to the TMPSP is superior to a disintegrated approach. Mixed-integer linear programming (MILP) models are developed to optimize the production schedule per period and crown pillar placement for the TMPSP. The MILP is implemented in Python and solved with the Gurobi (R) Optimizer. A case study is performed to validate the models, with a comparative analysis to obtain operational insights. The computational results show that the integrated model achieves 5% higher NPV than the disintegrated approach with less computational effort.
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    Optimizing transition: investigating the influence of operational parameters on production scheduling optimization for mines transitioning from open pit to block caving methods
    (2024) Flores, Ignacio Ortiz; Anani, Angelina; Li, Haitao; Jalilzadeh, Afrooz
    Current technologies have made the transition from surface to underground mining methods for mineral extraction feasible and economically viable. Determining the point of transition from one method to the other for deposits that are suitable to be exploited with both methods is challenging. The existing research integrates production scheduling optimization with determining the transition depth that maximizes net present value (NPV), potentially making the problem computationally intractable. Additionally, these studies do not consider some realistic operational constraints in the problem setting. This research proposes an integrated mixed-integer linear programming (MILP) model to investigate the extent to which operational constraints and parameters of transition mines affect the optimal production schedule and NPV of an operation. The authors have developed a computational experiment that evaluates development cost and rate, fleet size, stockpile, production footprint, dilution factor, and crown pillar placement on the model output. A case study is used to test and validate the model, with a comparative sensitivity analysis to obtain operational insights. Our work shows that the sensitivity of the NPV and computational time for each experimental factor varies significantly. There is no significant difference in NPV (0.15%) when the development cost is incorporated. However, for the fleet size, stockpile, and production footprint, an increase of 4.8%, 10.5%, and 4.8% respectively in the NPV can be achieved. The authors have concluded that the extent to which operational parameters and constraints of transition mines are accounted for, has a significant impact on the optimal production schedule and NPV obtained.