Optimising Stope Sequences for Long Term Mine Planning
CIM Edmonton 2004
Andre van Wageningen, David Muldonwey,
Long-term mine scheduling is one of the most difficult optimistion problems. The problem is said to be NP-complete in that the best possible solution cannot be found any quicker than checking all possible permutations and combinations. Although a number of techniques have been used in the past, many include significant simplifications or fail to produce acceptable results within the required timeframe. Genetic Algorithms (GAs) are among the most promising optimistion techniques within operations research. They are based on natural selection and evolution of solutions to complex problems. They have been known to quickly converge within a few percent of the optimal solution by examining only a fraction of the solution search space. The GA itself contains very little problem specific information and as such, has been linked to a discrete-event simulation (DES) for mine planning applications. The DES feeds information regarding the feasibility and quality of the mine sequence solutions back to GA during the optimization process. In this paper, the GA parameters are investigated, the DES is presented and the coupling of the two methods is outlined. The GA coupled DES is then used to “optimis” an example long term mine planning problem and the results are discussed.
Planning, Stope, Mine, Simulation, Optimisation, Sequence, Optimize