New Approach on the Development of Operational Fleet Management Systems Using Adaptative AI Techniques: Analysis of Adaptative Goal Weights

Modern surface mining operations are always seeking for minimizing costs while increasing production rates, among the optimization of other specific tasks that have an impact on the economics of the mine. Specifically, the cost related to the production fleet operation (shovels and trucks), the maximization of the overall performance of the equipment, and the uncertainty to meet the goals at the end of the operational time horizon are always topics of discussion with no apparent simple answer. On the other hand, truck-shovel dispatch systems based on linear/non-linear programming algorithms have been in operation since the mid-1980s with different levels of success depending on the correct arrangement of operational resources and the complexity of the mine, but having in most cases some important shortcomings related to the way these systems deal with the decision-making of the operational objectives, being necessary to have a human agent (dispatcher) to guide the system when the operational parameters change, creating the possibility of suboptimal decisions due to the human factor. This paper discusses the implementation of a novel methodology based on AI techniques to assist in the decision-making of the best goal selection when operating a Fleet Management System, focusing the analysis on the evaluation of variable goal weights in a non-preemptive multi-goal optimization model, comparing optimization outcomes to evaluate regions of optimal feasibility, and setting a training dataset to guide the machine learning algorithms in the prediction process, generating the best possible arrangement of goal weights for the optimization model.