A simulated annealing approach to mine production scheduling

Abstract

Increasing global competition, quality standards, environmental awareness and decreasing ore prices impose new challenges to mineral industries.Therefore, the extraction of mineral resources requires careful design and scheduling. In this research, simulated annealing (SA) is recommended to solve a mine production scheduling problem.First of all, in situ mineral characteristics of a deposit are simulated by sequential Gaussian simulation, and averaging the simulated characteristics within specified block volumes creates a three-dimensional block model.This model is used to determine optimal pit limits.A linear programming (LP) scheme is used to identify all blocks that can be included in the blend without violating the content requirements.The Lerchs–Grosmann algorithm using the blocks identified by the LP program determines optimal pit limits.All blocks that lie outside of the optimal pit limit are removed from the system and the blocks within the optimal pit are submitted to the production scheduling algorithm.Production scheduling optimization is carried out in two stages: Lagrangean parameterization, resulting in an initial sub-optimal solution, and multi-objective SA, improving the sub-optimal schedule further.The approach is demonstrated on a Western Australian iron ore body.