Intelligent Scheduling in Open-Pit Mining: A Multi-Agent System with Reinforcement Learning
Year: 2025
Type: article
Abstract: An important process in the mining industry is material handling, where trucks are responsible for transporting materials extracted by shovels to different locations within the mine. The decision about the destination of a truck is very important to ensure an efficient material handling operation. Currently, this decision-making process is managed by centralized systems that apply dispatching criteria. However, this approach has the disadvantage of not providing accurate dispatching solutions due to the lack of awareness of potentially changing external conditions and the reliance on a central node. To address this issue, we previously developed a multi-agent system for truck dispatching (MAS-TD), where intelligent agents representing real-world equipment collaborate to generate schedules. Recently, we extended the MAS-TD (now MAS-TDRL) by incorporating learning capabilities and compared its performance with the original MAS-TD, which lacks learning capabilities. This comparison was made using simulated scenarios based on actual data from a Chilean open-pit mine. The results show that the MAS-TDRL generates more efficient schedules.
Source: Machines
Authors: Gabriel Icarte-Ahumada, Otthein Herzog
Institutions: Arturo Prat University, Tongji University, University of Bremen
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409729851