Abstract
This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management.
Highlights
In short-term mine production planning, the truck dispatching activities aim to deliver the supply material, in terms of quantity and quality, being extracted from the mining fronts by the shovels to a destination
Reinforcement learning (RL) methods [15] provide means to make informed decisions under a variety of situations without retraining, as these methods learn from interacting with an environment and adapt to maximize a specific reward function
Most methods dealing with fleet optimization are applied to single mines, whereas an industrial mining complex is a set of integrated operations and facilities transforming geological resource supply into sellable products
Summary
In short-term mine production planning, the truck dispatching activities aim to deliver the supply material, in terms of quantity and quality, being extracted from the mining fronts by the shovels to a destination (e.g., processing facility, stockpile, waste dump). These elements can be observed in truck-dispatching activities in mining complexes, given that different processors have uncertain performances and that production targets may change, given the characteristics of the feeding materials. A common strategy to model the stochastic interactions between equipment and processors in an operating mining environment is through the use of discrete event simulation (DES) approaches [35,38,39,40,41,42,43].
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