4 présentations

• 
  10h30 - 10h55

  Underground Stope Optimization with Maximum Flow Method

  • Xiaoyu Bai, prés., École Polytechnique
  • Denis Marcotte, École Polytechnique
  • Richard Simon, École Polytechnique

  A novel stope optimizer for underground mining is presented. The optimizer is based on maximum flow method, and is analogous to the ultimate pit optimization in surface mining. It seeks to maximize the profit of stopes that respect the geometrical constraints. It can produce heuristic solutions for various deposit shapes mined with sublevel stoping method.

• 
  10h55 - 11h20

  Optimize a Mineral Supply Chain with Uncertainties Integrating Long-term Contracts

  • Jian Zang Ray, prés., Université McGill
  • Dimitrakopoulos Roussos, Université McGill

  A mining complex’s strategic and tactical plans for production and transportation are optimized in consideration of both contracted customers and the spot market. The proposed mixed-integer stochastic program model can be employed before signing a long-period sale contract to reduce risk due to the resource and market uncertainties.

• 
  11h20 - 11h45

  A Multi-Neighborhood Tabu Search Metaheuristic for Stochastic Production Scheduling

  • Renaud Senécal, prés., Université McGill
  • Roussos Dimitrakopoulos, COSMO Stochastic Mine Planning Laboratory, Université McGill

  A multi-neighborhood metaheuristic solution for open pit mine production scheduling with multiple destinations and supply (geological) uncertainty is presented. The optimization process provides the period (year) of extraction and a robust destination for materials mined from the deposit. A case study shows the computational advantages of the methods.

• 
  11h45 - 12h10

  Progressive Hedging Applied as a Metaheuristic to Schedule Production in Open-pit Mines Accounting for Metal Uncertainty

  • Amina Lamghari, prés., 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 solution approach based on Rockafellar and Wets’ progressive hedging algorithm. Computational experiments indicate the efficiency of the proposed approach in generating near-optimal solutions.