2nd Int'l Symposium on Mining Techniques of Narrow-Vein Deposits
David Muldowney, Paul Dunn,
Abstract Long-term mine scheduling is one of the most difficult optimization 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.

In current mining operations, it is important to report changes in long term plans as soon and as accurately as possible. Where geological data can change rapidly, such as in narrow vein deposits, the need to quickly identify and optimize different long term alternatives becomes a difficult task and can lead to many permutations of the long term mine plan. The question arises, is the new plan is optimal or not? (i.e. not only meets all the goals and constraints for the long term mine plan, but meets the company objectives).

Although a number of optimization 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 optimisation 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 possible permutations. The GA itself contains very little problem specific information and as such, has been linked to a discrete-event simulation (DES) which is able to evaluate the mine operation and schedules. The DES feeds information regarding the feasibility and quality of the solutions back to the GA during the optimisation process. In this paper, GAs and DES are described and the coupling of the two methods is outlined. The GA coupled DES is then used to “optimize” a problem and the results are discussed within a narrow vein mining context.
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