Application of agent-based modeling to truck-shovel dispatching systems in open-pit mines.

摘要

Various computer-based dispatching systems have been developed for managing truck and shovel pairing in surface mining operations and are being used to varying degrees of success. Systems have shown the ability to increase production and maintain ore quality within prescribed upper and lower limits provided there is a stable operational environment. However, operational environments in mining are uncertain and highly variable. Upsets such as equipment breakdowns, or changing weather conditions often occur and no claims have been made about the success of these systems to react to these upsets and successfully adapt to new operational conditions generated by upsets.; In an ant colony different activities are performed simultaneously by specialized individuals. However when the environment with an ant colony changes or experiences a major upset, the configuration of task allocations within the colony change to adapt to the new conditions.; In this thesis, the task allocation model developed for ant colonies was modified and used to develop a dispatch algorithm, the Agent Based Model, which reacts reliably to changes and upsets in surface mining operations. The algorithm was simulated over a twelve-hour shift using AUTOMOD ©, a discrete event simulation program. The simulation results of the Agent Based Model are compared to that of the Fixed Assignment Method used by current dispatch systems in which each truck is permanently assigned to a particular shovel. The total production of ore and waste from the Agent Based Model is consistently greater than that from the Fixed Assignment Method because an Ant Based system allows re-assignment (task reallocation). The simulations also show that the Agent Based system reliably adapts and limits the impact of upsets on a mining operation.