New approach on the development of operational fleet management systems using adaptative Al techniques: Analysis of adaptative goal weights

Truck-shovel dispatch systems based on linear or nonlinear 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. These systems are known for shortcomings related to the logic behind the decision-making of the operational objectives. This makes it 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. To address this issue, the present work discusses the implementation of a methodology based on artificial intelligence (AI) techniques to assist in the decision-making of the best optimization goals arrangement when operating a fleet management system. The focus of the analysis is on the evaluation of variable goal weights in a nonpreemptive, multigoal optimization model for multiple uncertain scenarios based on regions of optimal feasibility.