The excavating and haulage equipment selection to remove the overburden in an opencast mine is an essential and complex decision that requires substantial use of practical judgement and expertise as well as vast numerical calculations and extensive data collection about the equipment, their specifications and costs. Capturing and maintaining the knowledge of experts in the field is accomplished by the use of knowledge base systems. Such a knowledge relates mainly to the selection of the equipment in broad categories based on the geological, technical and environmental characteristics of the mine. To further identify the make, size and number of each piece of equipment that minimises the total cost of the operation, the problem needs to be presented as an optimisation model. This paper describes the development of an artificial intelligence technique, genetic algorithms, in order to find the input variables that can achieve the optimal cost. Genetic algorithms is a recent artificial technique inspired by the theory of evolution and biogenetic. Genetic algorithms has proven as a superior optimisation and search technique by emulating the process of natural evolution. Finally, the system is tested on a case study to validate its performance. A sensitivity analysis is performed on the case study in order to provide potential suggestions in areas where improvements could be made.
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