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Stochastic optimisation of open-pit mine production scheduling maximises the NPV of a mine's extraction sequence while satisfying production constraints and minimising deviations from targets. Past approaches consider mining block access based on slope constraints, thus providing no guarantee that the infrastructure required for equipment access is feasible. To address this issue, a joint stochastic optimisation of life-of-mine production scheduling and ramp design is proposed, ensuring feasible equipment access to the related mining blocks. An application at a gold mine demonstrates the value of considering the complex relationship that exists between ramp design, equipment access and production scheduling during optimisation. [ABSTRACT FROM AUTHOR]

Despite the upward trend in using semi-mobile in-pit crushing and conveying system (SMIPCC), some mining companies still hesitate to use SMIPCC in their operations. Therefore, a framework is needed to facilitate decision-making between SMIPCC and truck and shovel system (TS) and satisfy the location and relocation time of the semi-mobile crusher (SMC). For this purpose, this paper presents a mathematical integer programming (IP) model considering the operational constraints. The model specifies both the optimum location and optimum relocation time of SMC. Also, it compares the production schedule for the TS and SMIPCC utilised for ore and waste handling over the life of mine. Then, the system with maximum net present value (NPV) is selected as the optimal material handling system. Finally, the model is implemented with a case study. The results show that implementing SMIPCC instead of the TS system for ore and waste materials improves the NPV by 69.77%. [ABSTRACT FROM AUTHOR]

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 open-pit mine production scheduling (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 that 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. [ABSTRACT FROM AUTHOR]