Short-term mine production scheduling Research Papers (original) (raw)

A new short-term mine production scheduling formulation is developed herein based on stochastic integer programming. Unlike past approaches, the formulation simultaneously optimizes fleet and mining considerations, production extraction sequence and production constraints, while accounting for uncertainty in both orebody metal quantity and quality along with fleet parameters and equipment availability , all leading to a well-informed sequence of mining that is expected to have realistic as well as high performance during a mine's operation. To assess the latter performance and implementation intricacies of the proposed formulation, the formulation is applied at a multi-element iron mine and the resulting monthly schedules are assessed and compared to the conventional mine scheduling approach showing: lower cost, minable patterns, efficient fleet allocation ensuring higher and less variable utilization of the fleet.

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- Fleet management systems, Stochastic Optimization, Mine Production Scheduling, Short-term mine production scheduling

This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.