Evolutionary algorithms in large-scale open pit mine scheduling

Abstract

With many years of research and application to real-world problems, evolutionary algorithms (EAs) have solved various problems having thousands of variables, hard heuristic constraints, and complex evaluation procedures. This paper reports another successful application of EAs in open pit mine scheduling. Typically an ore body is discretized as a 3D block model which, depending on factors such as the amount of data obtained, size of deposit, block dimensions etc. can be made up of over one million blocks, thereby requiring an optimization algorithm to handle over a million variables. Open pit mine scheduling is a complex task which is subject to very strict hard geometrical and other practical mining constraints. To the best of our knowledge there are currently no algorithm or software package that can cater for the large number of constraints and sheer scale of the data sets represented by open pit mine scheduling. Most packages are limited in the size of block model and the kind of objective and constraint functions they can efficiently handle. The proposed optimization algorithm and the resulting software (evORElution-a trademark product of ORElogy) is developed by using the theoretical and fundamental results of evolutionary algorithms and has already been successfully used to produce complex multi-objective schedules for several large open pit iron ore mines involving hundreds of thousands to millions of variables.