Optimization of truck-shovel allocation in open-pit mines under uncertainty: a chance-constrained goal programming approach

Abstract

The truck allocation problem is an important section of the transportation system in open-pit mines. Most available models for the truck scheduling problem do not directly address the stochastic nature of truck-shovel systems by using multi-objective optimization techniques. This paper presents a chance-constrained goal programming (CCGP) model based on four important goals to estimate the impacts of the uncertainty on the efficiency of truck-shovel systems. The proposed model has been implemented using 11 schedule scenarios and different confidence levels (CLs) for loader's production to determine the best allocation of trucks in an open-pit copper mine. The results display that the model can handle the quality and quantity of material required to achieve the objectives of the short-term production schedule of the mine in all CLs, even in the highest risk level. This model has a remarkable ability to meet the required objectives in terms of uncertainty in mining operations.