Abstract
The mine production scheduling problem (MPSP) has been studied since the 1960s, and remains an active area of computational research. In extending the concepts of the MPSP, the automated mine may now be regarded as a hierarchical intelligent agent in which the bottom layer consists of distributed robotic equipment, while strategic functionality occupies the higher layers. Here we present a disambiguation of artificial intelligence, machine learning, computational optimization, and automation within the mining context. Specifically, the Q-learning algorithm has been adapted to generate the initial solutions for a high-performing strategic mine planning algorithm, originally developed by Lamghari, Dimitrakopoulos and Ferland, based on the variable neighbourhood descent (VND) metaheuristic. The hierarchical intelligent agent is presented as an integrative conceptual platform, defining the interaction between our new Q-learning adaptation and Lamghari's VND, and potentially other hierarchically controlled components of an artificially intelligent mine, having various degrees of automation. Sample computations involving Q-learning and VND are presented.
Highlights
Solving the mine production scheduling problem (MPSP) consists of identifying which blocks should be mined during each period within the life-of-mine to maximize the total net present value (NPV)
An artificial intelligence (AI) algorithm is introduced as a part of the computational framework, opening the discussion about how these algorithms contribute to finding the best way of solving complex problems, extending beyond the relatively nondescript MPSP
This paper introduced essential notions of AI and machine learning to embed strategic mine planning within the higher layers of an intelligent agent
Summary
Solving the mine production scheduling problem (MPSP) consists of identifying which blocks should be mined during each period within the life-of-mine to maximize the total net present value (NPV) This problem is divided into block-level resolution and aggregation methods (Campos, Arroyo, and Morales, 2018) and deterministic and stochastic versions (Lamghari, Dimitrakopoulos, and Ferland, 2014a; 2014b). For open-pit mining, managing stochasticity has shown significant improvements in expected NPV, increasing of the likeliness of meeting production forecast, and finding pit limits larger than those found by deterministic approaches (Lamghari, Dimitrakopoulos, and Ferland, 2014b) This is the main reason why stochastic optimization continues to be an active area of research (Navarra, Grammatikopoulos, and Waters, 2018a).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of the Southern African Institute of Mining and Metallurgy
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
