Agent-Based Optimization for Truck Dispatching in Open-Pit Mines

The mining industry has long recognized the value of dispatch systems in open pit mines as they reduce load and haul costs. Over the years, researchers have proposed many dispatch systems with various limitations and advantages. The simplest dispatch algorithms are the so called 1-truck-for-N-shovels dispatch strategy. These algorithms are limited by the fact that their objective functions do not consider all the objectives of a mine and cannot be applied to all possible truck-shovel configurations. They are also myopic in nature. However, they are simple and computationally efficient and do not require occasional updates of the upper stage problem as required in multi-stage dispatch algorithms. In this work, an agent-based truck dispatch algorithm that conceptualizes trucks as intelligent agents that make autonomous dispatching decisions to maximize their utility is proposed. The advantages of this algorithm includes utility functions that encapsulate all of management's objectives and agent's with broad situational awareness. They are also more suitable for autonomous trucks. We evaluate the new algorithm against a simple 1-truck-for-N-shovels dispatch strategies using discrete event simulation. The simulation results show that the new utility function has significant advantages over 1-truck-for-N-shovels inspired utility functions. Future work will incorporate adaptive behavior into the model via reinforcement learning algorithm.