A JOINT APPROACH TO THE IMPLEMENTATION OF FLEET MANAGEMENT SYSTEMS WITH STOCHASTIC SHORTTERM PRODUCTION SCHEDULING IN OPEN PIT MINES *L.J. Zamalloa1, R.G. Dimitrakopoulos2 1COSMO Stochastic Mine Planning Laboratory, McGill University, Montréal, QC Canada, (*Presenting author: lee.zamalloa@mail.mcgill.ca) 2COSMO Stochastic Mine Planning Laboratory, McGill University, Montréal, QC Canada, (roussos.dimitrakopoulos@mcgill.ca) ABSTRACT Modern implementations of operational fleet management systems in open pit mines seek the optimal use of fleet resources based on reliable information of mining block grades, production scheduling, grade control, and digging direction. As part of the management of the fleet, the allocation of shovels is typically a decision made along with the optimization of block scheduling based upon the short-term mine production plan and ore processing facility’s target grades. Meanwhile, truck dispatching becomes a reactive action to minimize fleet costs, while complying with the production sequence of extraction. This logic provides a daily fleet plan, where shovels are allocated to the production benches and trucks are matched to them, and actual truck tasks are decided by the dispatch operator, adjusting in real time the fleet resources to the needs of the mine. The typical dependency on a best estimate of the pre-blasting block grades creates the opportunity to consider scenarios of uncertainty related to the quality of the material in the muck piles after the blasting, because the head grades are commonly reported after delivery at the processing facility. Consequently, the imperfect information on material to be transported affects the use of the fleet, thus prioritizing areas that seem to be more beneficial according to the initial grade simulation. This may result in a first shovel allocation and truck dispatching plan that is likely to change once the reconciliated grades are reported from the processing facilities, leading to additional shovel movements and trucks rerouting. The uncertainty factors affecting the fleet tasks are not exclusive to the material grades, but also impact the availability and performance of the equipment, network of haul roads, power lines, primary crusher, etc., evaluating multiples scenarios of equally potential outcomes on which to decide. This research project evaluates the variability of shovel allocations and truck dispatch plans when uncertainty factors are modelled as stochastic scenarios, aiming to reduce the suboptimal use of fleet resources in a short-term environment, while controlling the risk of poorly estimated head grades of the material to be delivered to both stockpiles and processing facilities. KEYWORDS
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