4 présentations

• 
  10h30 - 10h55

  An Extended Stochastic Optimization Method for Multi-Process Mining Complexes

  • Luis Montiel, prés., McGill University

  A heuristic method is presented to generate life-of-mine production schedules accounting for geological uncertainty and considering extracting, stockpiling and processing decisions. The method improves an initial solution by swapping periods and destinations of mining blocks. Numerical tests indicate that the proposed method outperforms traditional deterministic approaches in terms of NPV.

• 
  10h55 - 11h20

  Mining Supply Chain Optimization under Geological Uncertainty

  • Ryan Goodfellow, prés., McGill University

  Mining complexes can be modelled as a supply chain from sources of material through processing streams to a set of products. This presentation proposes a simulation-optimization framework for optimizing mining supply chains and destination policies under geological uncertainty. This method is tested at Vale’s Onça Puma nickel laterite mining complex.

• 
  11h20 - 11h45

  Stochastic Network Flow Based Algorithm Applied to the Mine Production Schedule of Multi-Processor Open-Pit Mines.

  • Mario Silva, prés., McGill University
  • Roussos Dimitrakopoulos, COSMO Stochastic Mine Planning Laboratory, Université McGill

  We explore a new heuristic approach to solve a stochastic version of the mine production scheduling problem accounting for geological uncertainty. The methodology involves generating an initial solution by solving a series of sub-problems using either Branch-and-Cut or a greedy heuristic. Subsequently, this initial solution is improved using a network flow based algorithm.

• 
  11h45 - 12h10

  A Variable Neighborhood Descent Algorithm for the Open-Pit Mine Production Scheduling Problem with Metal Uncertainty

  • Amina Lamghari, prés., Université McGill
  • Roussos Dimitrakopoulos, COSMO Stochastic Mine Planning Laboratory, Université McGill

  We consider a stochastic version of the open-pit mine production scheduling problem, where the uncertainty stems from the orebody metal content. We propose a metaheuristic solution method based on variable neighborhood descent. Computational experiments indicate the efficiency of the proposed method in generating near-optimal solutions in reasonable computational times.