The optimization of open-pit mine production scheduling (OPMPS) is an intricate process due to its size and uncertainty of key input parameters. Over the last decade, substantial effort has been made towards the development of new stochastic frameworks that incorporate uncertainty into the decision process. However, due to the intrinsic complexity of the mathematical programming formulation and the large size of mineral deposits, finding an exact solution for OPMPS is likely intractable, motivating the development of new computationally efficient solution approaches.

In this paper, an efficient heuristic solution approach is applied and tested to the stochastic mine production scheduling of a relatively large gold deposit containing about 120 thousand blocks and considering a set of 15 geological scenarios generated stochastically. The case study addresses multiple processing streams and a 'grade' stockpile, which adds flexibility to the specific operation by advancing the processing of highly valuable material. The solution approach first generates an initial feasible solution by sequentially solving the stochastic OPMPS period by period, and then a network flow algorithm is used to sequentially search for improvements. In this network graph, the nodes identify candidate blocks which might have their extraction postponed or advanced, aiming for new schedules with higher value and lower risks. The results show that production schedules with low deviations from production expectations can be generated in a reasonable time for an actual mining environment.