Development of a scenario-based robust model for the optimal truck-shovel allocation in open-pit mining

Abstract We develop a scenario-based robust optimization (SBRO) approach to solve truck–shovel allocation (TSA) problem. To this end, we formulate the TSA problem in two phases by using the concepts of the SBRO approach, network analysis and the shortest path problem, and binary integer programming under uncertainties. We consider uncertainties in shovel output and crusher capacity from the first phase and number of available trucks from the second phase based on the SBRO approach. This TSA approach is applicable in all open-pit mines where trucks with different capacities are used, and different paths exist between loading and dumping points. We exemplify the applicability of the approach based on a copper mine data. We also compare the results of the SBRO approach with the current TSA of the studied mine. Then, we update the TSA formulation based on two new strategies of increasing shovel number and shovel capacity. Compared to the traditional strategy of the mine, the output of shovels increases to 6719, 10,000, and 12,500 tons/shift by the SBRO approach based on the strategies of the available equipment, increasing the number of shovels, and increasing the capacity of shovels, respectively. In addition, operational cost decreases to $1.1825, $0.8068, and $1.1238 per ton of ore based on the strategies, respectively.