Stochastic haul rate planning using two-stage stochastic programming and model predictive control

Abstract Optimising the scheduling of shovels and trucks is a critical task in open-pit mining, significantly impacting production efficiency and operational costs. Traditional models often assume ideal conditions without accounting for disturbances; however, in this paper, we address this gap by incorporating multiple uncertainties. These uncertainties are categorised into contingencies and variations. Contingencies are binary events that may or may not happen, such as shovel breakdowns. Variations are continuous factors such as fluctuations in mineral composition, truck travel time, maximum loading rate of shovels and maximum processing rate of crushers. We develop and test a robust mixed-integer programming (MILP) model for computing the optimal loading rate of shovels in open-pit mines. The problem is formulated as a two-stage stochastic optimisation problem which is then solved and executed in a receding horizon fashion. The proposed methodology is simulated for an open-pit mine in Australia’s Pilbara region and the results confirm the effectiveness of the proposed stochastic optimisation approach in minimising the effect of uncertainty on the operation.