Authors: A.Yu. Voronov, I.S. Syrkin, D.A. Pashkov

Title of the article: Intelligent fleet management systems in open-pit mining: status and prospects

Year: 2025, Issue: 4, Pages: 169-183

Branch of knowledge: 2.8.8 Geotechnology, mining machines

Index UDK: 622.23.05

DOI: 10.26730/1999-4125-2025-4-169-183

Abstract: Currently, in the field of equipment fleet management systems, which are an essential part of any open-pit mining enterprise, it is necessary to make the transition from traditional to intelligent systems due to the requirements of "Mining 4.0" and some of the disadvantages inherent in the traditional system. However, this transformation needs technical and strategic research. It is advisable to explore the integration of intelligence into career management systems because of its significant potential to increase productivity, reduce costs, and enhance the safety of open-pit mining. To this end, previously published studies of traditional and intelligent career management systems were studied in order to capture their current state. It is revealed that the main factors, parameters and optimization goals observed in existing conventional control systems make it possible to compare intelligent control systems with their conventional counterparts. After that, the basic intelligent models were compared in terms of five categories of equipment placement and dispatching functions, which makes it possible to identify ignored technical gaps. After determining the direction of future research, a popular SWOT analysis method is used to outline the strengths and weaknesses, as well as the opportunities and threats associated with the emergence of intelligent management systems in the careers of the future. Overall, the analysis shows that the advantages outweigh the disadvantages. In addition, possible solutions are proposed to eliminate existing shortcomings and threats. Some secondary goals are also pursued, such as a time-based analysis of the history of career management systems.

Key words: open-pit mining fleet management system artificial intelligence machine learning SWOT analysis

Receiving date: 01.06.2025

Approval date: 22.06.2025

Publication date: 28.08.2025

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