Integrated models of simulation and optimization for open pit mining planning

Short term mine plans must coordinate heterogeneous loading and haulage equipment while maintaining material availability, plant feed and product quality. This study examines an integrated decision support architecture in which mixed integer linear programming allocates equipment and trips and discrete event simulation evaluates execution under queues, failures and interruptions. The historical case concerns an August 2016 iron ore plan in Brazil with seven mining regions, two processing plants, three ore stockpiles and 34 trucks. A revised reference formulation makes capacity units, material conservation, size dependent quality and lexicographic priorities explicit. An independent arithmetic audit of the original embedded chart data compares three 29 day scenarios without rerunning the simulator. Total movement reaches 2.449, 2.551 and 2.635 million tonnes, respectively. The third scenario moves 7.60% more material than the first, but the configurations are not a nested fleet reduction experiment. Daily ore production remains variable, and only 46.2-50.0% of the available dry route grade records and 16.7-38.5% of wet route records lie within the plotted iron target bands. Conservation checks hold for the cached series; they do not establish operational validation. The contribution is a reproducible reanalysis and a clarified modelling framework that identifies where scheduling, quality control and simulation evidence require stronger support before operational deployment.

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Publication Details

Published
2026-11-04
DOI
https://doi.org/10.31224/8714
Primary Topic
Mining Techniques and Economics
Type
preprint
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preprint

Integrated models of simulation and optimization for open pit mining planning

PAULO FILIPE T. LOPES
Mining Techniques and Economics
preprint

Integrated models of simulation and optimization for open pit mining planning

PAULO FILIPE T. LOPES
preprint en

Abstract

Short term mine plans must coordinate heterogeneous loading and haulage equipment while maintaining material availability, plant feed and product quality. This study examines an integrated decision support architecture in which mixed integer linear programming allocates equipment and trips and discrete event simulation evaluates execution under queues, failures and interruptions. The historical case concerns an August 2016 iron ore plan in Brazil with seven mining regions, two processing plants, three ore stockpiles and 34 trucks. A revised reference formulation makes capacity units, material conservation, size dependent quality and lexicographic priorities explicit. An independent arithmetic audit of the original embedded chart data compares three 29 day scenarios without rerunning the simulator. Total movement reaches 2.449, 2.551 and 2.635 million tonnes, respectively. The third scenario moves 7.60% more material than the first, but the configurations are not a nested fleet reduction experiment. Daily ore production remains variable, and only 46.2-50.0% of the available dry route grade records and 16.7-38.5% of wet route records lie within the plotted iron target bands. Conservation checks hold for the cached series; they do not establish operational validation. The contribution is a reproducible reanalysis and a clarified modelling framework that identifies where scheduling, quality control and simulation evidence require stronger support before operational deployment.

Mining Techniques and Economics
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